91 articles matched your search for
Tags, Thresholds, Altruism, Evolution, Cooperation, Social Harmonics
Journal of Artificial Societies and Social Simulation 1 (1) 4
Kyeywords: Cooperation, Evolutionary Models, Artificial Agents, Altruism
Abstract: How does social order emerge among autonomous but interdependent agents? The expectation of future interaction may explain cooperation based on rational foresight, but the "shadow of the future" offers little leverage on the problem of social order in "everyday life" -- the habits of association that generate unthinking compliance with social norms. Everyday cooperation emerges not from the shadow of the future but from the lessons of the past. Rule-based evolutionary models are a promising way to formalize this process. These models may provide new insights into emergent social order -- not only prudent reciprocity, but also expressive and ritual self-sacrifice for the welfare of close cultural relatives.
Journal of Artificial Societies and Social Simulation 1 (1) 5
Kyeywords: Cooperation, Altruism, Rationality, Cognition
Abstract: This is a response to Michael Macy's contribution to the JASSS Forum, Social Order in Artificial Worlds.
Journal of Artificial Societies and Social Simulation 1 (3) 2
Kyeywords: Evolutionary Algorithms, Genetic Programming, Social Evolution, Selectionist Paradigm
Abstract: This paper attempts to illustrate the importance of a coherent behavioural interpretation in applying evolutionary algorithms like Genetic Algorithms and Genetic Programming to the modelling of social processes. It summarises and draws out the implications of the Neo-Darwinian Synthesis for processes of social evolution and then discusses the extent to which evolutionary algorithms capture the aspects of biological evolution which are relevant to social processes. The paper uses several recent papers in the field as case studies, discussing more and less successful uses of evolutionary algorithms in social science. The key aspects of evolution discussed in the paper are that it is dependent on relative rather than absolute fitness, it does not require global knowledge or a system level teleology, it avoids the credit assignment problem, it does not exclude Lamarckian inheritance and it is both progressive and open ended.
José Castro Caldas and Helder Coelho
Journal of Artificial Societies and Social Simulation 2 (2) 1
Kyeywords: Institutional Economics, Agent Modelling, Socio-Economic Simulation, Evolutionary Algorithms
Abstract: Institutions, the way they are related to the behaviour of the agents and to the aggregated performance of socio-economic systems, are the topic addressed by this essay. The research is based on a particular concept of a bounded rational agent living in society and by a population based simulation model that describes the processes of social learning. From simple co-ordination problems, where conventions spontaneously emerge, to situations of choice over alternative constitutional rules, simulation was used as a means to test the consistency and extract the implications of the models. Institutions, as solutions to recurring problems of social interaction, are both results and preconditions for social life, unintended outcomes and human devised constraints. In an evolutionary setting no support is found for the deep rooted beliefs about the 'naturally' beneficial outcomes generated by 'invisible-hand' processes or by any alternative Hobbesian meta-agency.
Gérard Ballot and Erol Taymaz
Journal of Artificial Societies and Social Simulation 2 (2) 3
Kyeywords: Technological Change, Human Capital, Endogenous Growth, Artificial Intelligence, Artificial Worlds, Classifier Systems, Microsimulation, Evolutionary Theory
Abstract: The purpose of the paper is to model the process of rule generation by firms that must allocate their resources between physical assets, training, and R&D, and to study the microeconomic performances as well as the aggregate outcomes. The framework is a complete micro-macroeconomic Leontieff-Keynesian model initialised with Swedish firms, and provides one of the first applications of the " artificial world " methodology to a complete economic system. The model also displays detailed features of technological change and firms' human capital. In this complex and evolving Schumpeterian environment, firms are "boundedly rational" and use rules. They learn better rules to survive, and we model this process with the use of classifiers. We are able to show that the diversity of rules is sustained over time, as well as the heterogeneity of firms' performances. Simple rules appear to secure larger market shares than complex rules. The learning process improves macroeconomic performance to a large extent whereas barriers to entry are also detrimental for macroeconomic performance.
Barry G. Lawson and Steve Park
Journal of Artificial Societies and Social Simulation 3 (1) 2
Kyeywords: Artificial Society, Discrete-Event Simulation, Synchronous Time Evolution, Simulation Artifacts, Asynchronous Time Evolution, Next-Event Simulation, Event List Processing
Abstract: "Artificial society" refers to an agent-based simulation model used to discover global social structures and collective behavior produced by simple local rules and interaction mechanisms. In most artificial society discrete-event simulation models, synchronous time evolution is used to drive the actions and interactions of the landscape and agents. Although for some applications synchronous time evolution is the correct modeling approach, other applications are better suited for asynchronous time evolution. In this paper we demonstrate that very different behavior can be observed in a typical artificial society model if agent events occur asynchronously. Using an adaptation of the artificial society model defined by Epstein and Axtell, we describe the implementation of asynchronous time evolution in a discrete-event simulation model. With output from this model, we show that the use of asynchronous time evolution can eliminate potential simulation artifacts produced using synchronous time evolution. Since the use of discrete-event simulation can produce an associated loss in computational performance, we also discuss means of improving the performance of the artificial society simulation model. We provide results demonstrating that acceptable computational performance for asynchronous time evolution can be achieved using an appropriate event list implementation.
Evelien Zeggelink, Henk de Vos and Donald Elsas
Journal of Artificial Societies and Social Simulation 3 (3) 1
Kyeywords: Reciprocal Altruism, Group Living, Segmentation
Abstract: To what degree does reciprocal altruism add to the explanation of the human way of group living? That is the main question of this paper. In order to find an answer to this question, we use the Social Evolution Model (SEM) that has been developed earlier. It allows us to investigate both the conditions under which cooperation is a viable strategy and the conditions under which individuals structure themselves in stable groups. In the SEM, exchange relationships are created on the basis of asking for help and providing support in an initially unstructured population. We study whether, and to what extent, this process results in a socially segmented population. First we arrive at the conclusion that there is no analytical solution to some minimal group size that guarantees group survival in which all individuals are reciprocal altruists. If there is anything, then it is an optimal instead of a minimal group size. Our simulation results suggests that on the basis of our present assumptions some degree of group formation does appear, but not to the extent that we 'see' groups in real life exchange settings.
Ramzi Suleiman and Ilan Fischer
Journal of Artificial Societies and Social Simulation 3 (4) 1
Kyeywords: Prisoner's Dilemma, Intergroup Conflict, Evolution of Cooperation, Social Influence, Representation, Elections Frequency
Abstract: The study explores the evolution of decision strategies and the emergence of cooperation in simulated societies. In the context of an inter-group conflict, we simulate three different institutions for the aggregation of attitudes. We assume that: (a) the conflict can be modeled as an iterated Prisoner's Dilemma played by two decision makers, each representing her group for a fixed duration; (b) the performance of each group's representative influences her group members and, consequently, her prospects to be reelected. Our main objectives are: (1) to investigate the effects of three power-delegation mechanisms: Random Representation, Mean Representation, and Minimal Winning Coalition representation, on the emergence of representatives' decision strategies, (2) to investigate the effect of the frequency of elections on the evolving inter-group relations. Outcomes of 1080 simulations show that the emergence of cooperation is strongly influenced by the delegation mechanism, the election frequency, and the interaction between these two factors.
Journal of Artificial Societies and Social Simulation 3 (4) 3
Kyeywords: Game Theory, Classical Iterated Prisoner's Dilemma, Cooperation
Abstract: This paper reports results obtained with a strategy for the Iterated Prisoner's Dilemma. The paper describes a strategy that tries to incorporate a technique to forgive strategies that have defected or retaliated, in the hope of (re-)establishing cooperation. The strategy is compared to well-known strategies in the domain and results presented. The initial findings, as well as echoing past findings, provides evidence to suggest a higher degree of forgiveness can be beneficial and may result in greater rewards.
J. P. Marney and Heather F.E. Tarbert
Journal of Artificial Societies and Social Simulation 3 (4) 4
Kyeywords: Reciprocal Altruism, Group Living, Segmentation
Abstract: The purpose of this paper is to argue for clarity of methodology in social science simulation. Simulation is now at a stage in the social sciences where it is important to be clear why simulation should be used and what it is intended to achieve. The paper goes on to discuss a particularly important source of opposition to simulation in the social sciences which arises from perceived threats to the orthodox hard-core. This is illustrated by way of a couple of case studies. The paper then goes on to discuss defences to standard criticisms of simulation and the various positive reasons for using simulation in preference to other methods of theorising in particular situations.
Klaus Auer and Timothy Norris
Journal of Artificial Societies and Social Simulation 4 (1) 6
Kyeywords: Cellular Automata, Multi-Agent Model, Evolution, Social Networks, Object Oriented Programming Language, Artificial Landscape
Abstract: The behavior of cellular automata is a very close representation of the evolution of complex social systems. We developed the simulation model "ArrierosAlife" to explore the behavior of changes in social networks over time. The model is based on empirical data, a result out of a longitudinal field work. The focus of this research is a comparison of network changes over time in the "real world" compared with the emergence of social networks in an artificial society. "Ascape" was used as a modeling frame work to facilitate the development and analysis of the simulation model. We will give a brief overview of the developed model and describe the experiences using "Ascape" as a framework.
Wolfgang Kerber and Nicole J. Saam
Journal of Artificial Societies and Social Simulation 4 (3) 2
Kyeywords: Competition, Hayek, Knowledge, Innovation, Merger Control, Concentration, Simulation, Lock-In, Evolutionary Economics
Abstract: Hayek's well-known evolutionary concept of "competition as a discovery procedure" can be characterized as a parallel process of experimentation, in which rivalrous firms generate and test hypotheses about the best way to fulfill the consumers' preferences. Through this permanent process of variation and selection of hypotheses (innovation / imitation) a process of knowledge accumulation can take place. The central aim of our paper is to model the basic Hayekian learning mechanism, which consists of experimentation and mutual learning, and to ask for determinants of the rapidity of knowledge accumulation. In our multilevel simulation model, on the micro level, firms create new hypotheses through mutation. On the macro level, on the market, these hypotheses meet and the best firm is determined. All firms then imitate the best firm. In our model, 100 of these periods which consist of an innovation and an imitation phase are simulated. We presume that decentrality is crucial for the working of the knowledge-generating process, because a larger number of independently innovating firms leads to more experimentation. We investigate into the impact of firm concentration, the impact of the decentralization of firms, as well as the impact of impediments in imitation like lock-ins on the growth rate of knowledge accumulation. Our simulation results show that the number of firms is positively correlated with the rapidity of knowledge accumulation suggesting a new argument for a critical assessment of mergers in competition policy.
Journal of Artificial Societies and Social Simulation 4 (3) 4
Kyeywords: Industrial Clusters, Simulations, Evolution, Spatial Agglomeration
Abstract: Localised industrial clusters have received much attention in economic research in the last decade. They are seen as one of the reasons for the economic success of certain regions in comparison to others. This paper studies the evolution of such industrial clusters. To this end, a spatial structure of regions is set up and the entry, exit, and growth of firms within these regions is modelled and studied with the help of simulations. Several mechanisms that are often stated to be important in the context of localised industrial clusters are explicitly modelled. The influence of these mechanisms on the geographical concentration of industries is studied.
Marie-Edith Bissey and Guido Ortona
Journal of Artificial Societies and Social Simulation 5 (2) 2
Kyeywords: Cooperation, Conventions, Prisoner's Dilemma, Social Simulation, SWARM
Abstract: This paper describes a study of the robustness of cooperative conventions. We observe the effect of the invasion of non-cooperating subjects into a community adopting a cooperative convention. The convention is described by an indefinitely repeated prisoner-dilemma game. We check the effects on the robustness of the cooperating convention of two characteristics of the game, namely the size of the prisonner-dilemma groups and the "intelligence" of the players. The relevance for real-world problems is considered. We find that the "intelligence" of the players plays a crucial role in the way players learn to cooperate. The simulation program is written in SWARM (Java version).
Journal of Artificial Societies and Social Simulation 5 (3) 3
Kyeywords: Altruism, mutualism, generosity, economy, interactions
Abstract: Would society be better off, in aggregate economic terms, if altruism was more widely practiced among its members? Here I try to answer this question using an agent based computer simulation model of a simple agricultural society. A Monte Carlo exploration of the parameter landscapes allowed the exploration of the range of possible situations of conflict between the individual and the group. The possible benefit of altruism on the aggregate wealth of society was assessed by comparing the overall efficiency of the system in accumulating aggregate utility in simulations with altruistic agents, and with equivalent systems where no altruistic acts were allowed. The results show that no simple situation could be found where altruistic behavior was beneficial to the group. Dissipative and equitative altruistic behavior was detrimental to the aggregate wealth of the group or was neutral. However, the modeling of non-economic factors or the inclusion of a synergic effect in the mutualistic interactions did increase the aggregated utility achieved by the virtual society.
Journal of Artificial Societies and Social Simulation 6 (1) 7
Kyeywords: Strategy, Evolution, Learning, Genetic-algorithm, Tit-For-Tat, Noise, Errors
Abstract: The study examines two approaches to the development of behavioral strategies: i) the evolutionary approach manifested in a Genetic Algorithm, which accounts for gradual development and simultaneous refinement of an entire population; and ii) the behavioral learning approach, which focuses on reinforcements at the individual's level. The current work is part from an ongoing project dealing with the development of strategic behavior. The reported study evaluates the potential of differential reinforcements to provide probabilistic noisy Tit-For-Tat strategies with the motivation to adopt a pure Tit-For-Tat strategy. Results show that provocability and forgiveness, the traits that account for Tit-For-Tat's successes, also prevent it from gaining relative fitness and become an attractor for noisy (non-perfect) Tit-For-Tat strategies.
Journal of Artificial Societies and Social Simulation 6 (2) 4
Kyeywords: Cooperation, complex adaptive agents, prisoner?s dilemma, game theory, evolutionary model
Abstract: The self-organization into cooperative regimes of a system of "selfish" agents playing the pairwise Prisoner's Dilemma game (PDG) is analyzed using a simple agent-based model. At each time step t, the agents divide into those who cooperate (C) and those who defect (D). The agents have no memory and use strategies not based on direct reciprocity nor 'tags'. Only one dynamical variable is assigned to each agent, namely his income at time t dC(t) obtained by playing the PDG with a partner chosen at random. A simple adapting strategy for the behavior of the agents (C or D) is shown to give rise, for a wide variety of PD payoff matrices, to a cooperative regime resistant to ?always D? strategy.
Javier Pajares, Adolfo López-Paredes and Cesáreo Hernández-Iglesias
Journal of Artificial Societies and Social Simulation 6 (2) 7
Kyeywords: Computational Organization, Multi-agent Systems, Industrial Dynamics, Cognitive Evolutionary Models
Abstract: We present a model of the dynamics of the industry for innovation and R&D management. It is a novel view of the evolution of industry, both in theoretical and computational terms. Industries are seen as organisations of firms and consumers, and the aggregated behaviour is the consequence of the decisions taken by the individual firms. A multi-agent system is designed and programmed to model the industry. The agents are the individual firms and the consumers are represented by the group demands for two products: a new and an old one. The strategic decisions of the firms are the patterns of investment in process and product innovation, and whether to stay or to leave the industry. Agents information and incentives in terms of assets returns are specified. Co-ordination is embedded into the model. We include in the model the relevant ideas from evolutionary economics in the product life cycle context. The model is used to explain and to reproduce the main observed facts in life cycle dynamics, in terms of the rise and fall of incumbent firms, concentration ratios, R&D expenditure and path-dependence in the evolution of the industry. Thus showing the utility of MAS in industrial evolutionary modeling and policy assessment.
Andreas Pyka and Petra Ahrweiler
Journal of Artificial Societies and Social Simulation 7 (2) 6
Kyeywords: Emaee, Evolutionary Economics, Conference Proceedings
Abstract: At the third European Meeting on Applied Evolutionary Economics in Augsburg almost 120 participants from all over Europe, North and South America, and South Africa discussed the latest developments in applied Evolutionary Economics. In addition to the nine keynote lectures covering a wide range of topics addressed to the conference theme, 72 papers were presented in the parallel sessions. Due to the general high quality of papers and also an increasing share of simulation work we decided to have this time not only our conference proceedings (Pyka and Hanusch 2004)) but also a special issue in a well recognized journal. And of course, no other journal than JASSS would fit better to our EMAEE initiative. Finally, out of the 72 papers eight jointly suggested by the EMAEE scientific committee were chosen to be included in the regular referee process of JASSS. In the end, five dealing innovatively with simulation models were chosen for this special issue.
Javier Pajares, Cesáreo Hernández-Iglesias and Adolfo López-Paredes
Journal of Artificial Societies and Social Simulation 7 (2) 7
Kyeywords: Evolutionary economics, agent-based, muti-agent systems, innovation, research and development, industry evolution, knowledge-based economy
Abstract: Evolutionary arguments are an appropriate approach to the analysis of industry dynamics in a knowledge-based economy, because they can deal properly with innovation processes, technological change, path-dependence and knowledge. But in order to formalise all of this verbal accounting, researchers need methodological tools which support their theoretical analysis. In this paper we suggest some of the main requirements for computer simulation to have the same standing as the traditional tools used by neoclassical economists. Among others, aggregated behaviour should “emerge” from micro-foundations, economic agents should exhibit bounded rational behaviour, learning must be endogenous and human learning should be in agreement with some stylised facts from cognitive science and psychology. We argue that multi-agent systems is a methodology which fulfills some of the requirements above. We also propose an alternative way for modelling cognitive learning in evolutionary environments, which is in agreement with some basic concepts from cognitive science. Agents are endowed with both declarative and procedural knowledge. We have used our approach to build evolutionary models of innovative industries, where firms learn how to change their decisions about R&D budget, production, technology, etc. We refer as well to some applications using the same framework to model behavioural financial markets, economic geography and water resource management.
Luis R. Izquierdo, Nicholas M. Gotts and J. Gareth Polhill
Journal of Artificial Societies and Social Simulation 7 (3) 1
Kyeywords: Social Dilemmas, Case-Based Reasoning, Prisoner's Dilemma, Agent-Based Simulation, Analogy, Game Theory, Aspiration Thresholds, Equilibrium
Abstract: In this paper social dilemmas are modelled as n-player games. Orthodox game theorists have been able to provide several concepts that narrow the set of expected outcomes in these models. However, in their search for a reduced set of solutions, they had to pay a very high price: they had to make disturbing assumptions such as instrumental rationality or common knowledge of rationality, which are rarely observed in any real-world situation. We propose a complementary approach, assuming that people adapt their behaviour according to their experience and look for outcomes that have proved to be satisfactory in the past. These ideas are investigated by conducting several experiments with an agent-based simulation model in which agents use a simple form of case-based reasoning. It is shown that cooperation can emerge from the interaction of selfish case-based reasoners. In determining how often cooperation occurs, aspiration thresholds, the agents' representation of the world, and their memory all play an important and interdependent role. It is also argued that case-based reasoners with high enough aspiration thresholds are not systemically exploitable, and that if agents were sophisticated enough to infer that other players are not exploitable either, they would eventually cooperate.
Pieter Buzing, A.E. Eiben and Martijn C. Schut
Journal of Artificial Societies and Social Simulation 8 (1) 2
Kyeywords: Social Simulation, Communication, Cooperation, Artificial Societies
Abstract: The main contribution of this paper is threefold. First, it presents a new software system for empirical investigations of evolving agent societies in SugarScape like environments. Second, it introduces a conceptual framework for modeling cooperation in an artificial society. In this framework the environmental pressure to cooperate is controllable by a single parameter, thus allowing systematic investigations of system behavior under varying circumstances. Third, it reports upon results from experiments that implemented and tested environments based upon this new model of cooperation. The results show that the pressure to cooperate leads to the evolution of communication skills facilitating cooperation. Furthermore, higher levels of cooperation pressure lead to the emergence of increased communication.
Hugo Fort and Nicolás Pérez
Journal of Artificial Societies and Social Simulation 8 (3) 1
Kyeywords: Complex Adaptive Agents, Cooperation, Artificial Societies, Spatial Game Theory
Abstract: Cooperation among self-interested individuals pervades nature and seems essential to explain several landmarks in the evolution of live organisms, from prebiotic chemistry through to the origins of human societies. The iterated Prisoner's Dilemma (IPD) has been widely used in different contexts, ranging from social sciences to biology, to elucidate the evolution of cooperation. In this work we approach the problem from a different angle. We consider a system of adaptive agents, in a two dimensional grid, playing the IPD governed by Pavlovian strategies. We investigate the effect of different possible measures of success (MSs) used by the players to assess their performance in the game. These MSs involve quantities such as: the utilities of a player in each round U, his cumulative score (or "capital" or \'wealth\') W, his neighbourhood "welfare" and combinations of them. The agents play sequentially with one of their neighbours and the two players update their "behaviour" (C or D) using fuzzy logic which seems more appropriate to evaluate an imprecise concept like "success" than binary logic. The steady states are characterised by different degrees of cooperation, "economic geographies" (population structure and maps of capital) and "efficiencies" which depend dramatically on the MS. In particular, some MSs produce patterns of "segregation" and "exploitation".
José Manuel Galán and Luis R. Izquierdo
Journal of Artificial Societies and Social Simulation 8 (3) 2
Kyeywords: Replication, Agent-Based Modelling, Evolutionary Game Theory, Social Dilemmas, Norms, Metanorms
Abstract: In this paper we try to replicate the simulation results reported by Axelrod (1986) in an influential paper on the evolution of social norms. Our study shows that Axelrod's results are not as reliable as one would desire. We can obtain the opposite results by running the model for longer, by slightly modifying some of the parameters, or by changing some arbitrary assumptions in the model. This re-implementation exercise illustrates the importance of running stochastic simulations several times for many periods, exploring the parameter space adequately, complementing simulation with analytical work, and being aware of the scope of our simulation models.
Journal of Artificial Societies and Social Simulation 9 (1) 10
Kyeywords: Evolution, Tags, Group, Symbiosis, Specialisation, Emergence
Abstract: This paper presents a evolutionary simulation where the presence of 'tags' and an inbuilt specialisation in terms of skills result in the development of 'symbiotic' sharing within groups of individuals with similar tags. It is shown that the greater the number of possible sharing occasions there are the higher the population that is able to be sustained using the same level of resources. The 'life-cycle' of a particular cluster of tag-groups is illustrated showing: the establishment of sharing; a focusing-in of the cluster; the exploitation of the group by a particular skill-group and the waning of the group. This simulation differs from other tag-based models in that is does not rely on either the forced donation of resources to individuals with the same tag and where the tolerance mechanism plays a significant part. These 'symbiotic' groups could provide the structure necessary for the true emergence of artificial societies, supporting a division of labour similar to that found in human societies.
István Back and Andreas Flache
Journal of Artificial Societies and Social Simulation 9 (1) 12
Kyeywords: Interpersonal Commitment, Fairness, Reciprocity, Agent-Based Simulation, Help Exchange, Evolution
Abstract: A prominent explanation of cooperation in repeated exchange is reciprocity (e.g. Axelrod, 1984). However, empirical studies indicate that exchange partners are often much less intent on keeping the books balanced than Axelrod suggested. In particular, there is evidence for commitment behavior, indicating that people tend to build long-term cooperative relationships characterised by largely unconditional cooperation, and are inclined to hold on to them even when this appears to contradict self-interest. Using an agent-based computational model, we examine whether in a competitive environment commitment can be a more successful strategy than reciprocity. We move beyond previous computational models by proposing a method that allows to systematically explore an infinite space of possible exchange strategies. We use this method to carry out two sets of simulation experiments designed to assess the viability of commitment against a large set of potential competitors. In the first experiment, we find that although unconditional cooperation makes strategies vulnerable to exploitation, a strategy of commitment benefits more from being more unconditionally cooperative. The second experiment shows that tolerance improves the performance of reciprocity strategies but does not make them more successful than commitment. To explicate the underlying mechanism, we also study the spontaneous formation of exchange network structures in the simulated populations. It turns out that commitment strategies benefit from efficient networking: they spontaneously create a structure of exchange relations that ensures efficient division of labor. The problem with stricter reciprocity strategies is that they tend to spread interaction requests randomly across the population, to keep relations in balance. During times of great scarcity of exchange partners this structure is inefficient because it generates overlapping personal networks so that often too many people try to interact with the same partner at the same time.
Peter Andras, John Lazarus, Gilbert Roberts and Steven J Lynden
Journal of Artificial Societies and Social Simulation 9 (1) 7
Kyeywords: Agent-Based Modelling, Cooperation, Social Interaction Simulation, Uncertainty
Abstract: Uncertainty is an important factor that influences social evolution in natural and artificial environments. Here we distinguish between three aspects of uncertainty. Environmental uncertainty is the variance of resources in the environment, perceived uncertainty is the variance of the resource distribution as perceived by the organism and effective uncertainty is the variance of resources effectively enjoyed by individuals. We show analytically that perceived uncertainty is larger than environmental uncertainty and that effective uncertainty is smaller than perceived uncertainty, when cooperation is present. We use an agent society simulation in a two dimensional world for the generation of simulation data as one realisation of the analytical results. Together with our earlier theoretical work, results here show that cooperation can buffer the detrimental effects of uncertainty on the organism. The proposed conceptualisation of uncertainty can help in understanding its effects on social evolution and in designing artificial social environments.
Alberto Acerbi and Domenico Parisi
Journal of Artificial Societies and Social Simulation 9 (1) 9
Kyeywords: Artificial Life, Cultural Transmission, Cultural Evolution, Horizontal Cultural Transmission
Abstract: We describe some simulations that compare cultural transmission between and within generations (inter-generational vs intra-generational transmission) in populations of embodied agents controlled by neural networks. Our results suggest that intra-generational transmission has the role of adding variability to the evolutionary process and that this function seems particularly useful when the population lives in a rapidly changing environment. Adaptation to environmental change is slower if cultural transmission is purely inter-generational while it is faster if a certain amount of intra-generational cultural transmission makes it possible to remove earlier and no longer suitable behaviors, facilitating the emergence of new and more appropriate ones.
David Joyce, John Kennison, Owen Densmore, Stephen Guerin, Shawn Barr, Eric Charles and Nicholas S. Thompson
Journal of Artificial Societies and Social Simulation 9 (2) 4
Kyeywords: Game Theory; Altruism; Prisoners' Dilemma; TIT FOR TAT; MOTH; Docking; Netlogo
Abstract: There are three prominent solutions to the Darwinian problem of altruism, kin selection, reciprocal altruism, and trait group selection. Only one, reciprocal altruism, most commonly implemented in game theory as a TIT FOR TAT strategy, is not based on the principle of conditional association. On the contrary, TIT FOR TAT implements conditional altruism in the context of unconditionally determined associates. Simulations based on Axelrod\'s famous tournament have led many to conclude that conditional altruism among unconditional partners lies at the core of much human and animal social behavior. But the results that have been used to support this conclusion are largely artifacts of the structure of the Axelrod tournament, which explicitly disallowed conditional association as a strategy. In this study, we modify the rules of the tournament to permit competition between conditional associates and conditional altruists. We provide evidence that when unconditional altruism is paired with conditional association, a strategy we called MOTH, it can out compete TIT FOR TAT under a wide range of conditions.
Journal of Artificial Societies and Social Simulation 9 (2) 5
Kyeywords: Social Cognition, Imitation, Cultural Co-Evolution, Differentiation, Reflexivity, Metacognition, Stochastic Game Theory, Endogenous Distributions, Metamimetic Games, Counterfactual Equilibrium
Abstract: Imitation is fundamental in the understanding of social systems' dynamics. But the diversity of imitation rules employed by modelers proves that the modeling of mimetic processes cannot avoid the traditional problem of endogenization of all the choices, including the one of the mimetic rules. Starting from the remark that metacognition and human reflexive capacities are the ground for a new class of mimetic rules, we propose a formal framework, metamimetic games, that enables to endogenize the distribution of imitation rules while being human specific. The corresponding concepts of equilibrium — counterfactually stable state — and attractor are introduced. Finally, we give an interpretation of social differenciation in terms of cultural co-evolution among a set of possible motivations, which departs from the traditional view of optimization indexed to immutable criteria that exist prior to the activity of agents.
Nigel Gilbert, Matthijs den Besten, Akos Bontovics, Bart G.W. Craenen, Federico Divina, A.E. Eiben, Robert Griffioen, György Hévízi, Andras Lõrincz, Ben Paechter, Stephan Schuster, Martijn C. Schut, Christian Tzolov, Paul Vogt and Lu Yang
Journal of Artificial Societies and Social Simulation 9 (2) 9
Kyeywords: Artificial Societies, Evolution of Language, Decision Trees, Peer-To-Peer Networks, Social Learning
Abstract: The NewTies project is implementing a simulation in which societies of agents are expected to de-velop autonomously as a result of individual, population and social learning. These societies are expected to be able to solve environmental challenges by acting collectively. The challenges are in-tended to be analogous to those faced by early, simple, small-scale human societies. This report on work in progress outlines the major features of the system as it is currently conceived within the project, including the design of the agents, the environment, the mechanism for the evolution of language and the peer-to-peer infrastructure on which the simulation runs.
Klaus Jaffe and Roberto Cipriani
Journal of Artificial Societies and Social Simulation 10 (1) 7
Kyeywords: Social Simulation, Interactions, Group Size, Selfish Heard, Cultural Evolution, Biological Evolution
Abstract: A one dimensional cellular automata model describes the evolutionary dynamics of cooperation when grouping by cooperators provides protection against predation. It is used to compare the dynamics of evolution of cooperation in three settings. G: only vertical transmission of information is allowed, as an analogy of genetic evolution with heredity; H: only horizontal information transfer is simulated, through diffusion of the majority\'s opinion, as an analogy of opinion dynamics or social learning; and C: analogy of cultural evolution, where information is transmitted both horizontally (H) and vertically (V) so that learned behavior can be transmitted to offspring. The results show that the prevalence of cooperative behavior depends on the costs and benefits of cooperation so that: a- cooperation becomes the dominant behavior, even in the presence of free-riders (i.e., non-cooperative obtaining benefits from the cooperation of others), under all scenarios, if the benefits of cooperation compensate for its cost; b- G is more susceptible to selection pressure than H achieving a closer adaptation to the fitness landscape; c- evolution of cooperative behavior in H is less sensitive to the cost of cooperation than in G; d- C achieves higher levels of cooperation than the other alternatives at low costs, whereas H does it at high costs. The results suggest that a synergy between H and V is elicited that makes the evolution of cooperation much more likely under cultural evolution than under the hereditary kind where only V is present.
András Németh and Károly Takács
Journal of Artificial Societies and Social Simulation 10 (3) 4
Kyeywords: Altruism, Teaching, Knowledge Transfer, Spatially Structured Social Dilemmas
Abstract: The evolution of altruism in humans is still an unresolved puzzle. Helping other individuals is often kinship-based or reciprocal. Several examples show, however, that altruism goes beyond kinship and reciprocity and people are willing to support unrelated others even when this is at a cost and they receive nothing in exchange. Here we examine the evolution of this "pure" altruism with a focus on altruistic teaching. Teaching is modeled as a knowledge transfer which enhances the survival chances of the recipient, but reduces the reproductive efficiency of the provider. In an agent-based simulation we compare evolutionary success of genotypes that have willingness to teach with those who do not in two different scenarios: random matching of individuals and spatially structured populations. We show that if teaching ability is combined with an ability to learn and individuals encounter each other on a spatial proximity basis, altruistic teaching will attain evolutionary success in the population. Settlement of the population and accumulation of knowledge are emerging side-products of the evolution of altruism. In addition, in large populations our simple model also produces a counterintuitive result that increasing the value of knowledge keeps fewer altruists alive.
Stéphane Airiau, Sabyasachi Saha and Sandip Sen
Journal of Artificial Societies and Social Simulation 10 (3) 7
Kyeywords: Repeated Games, Evolution, Simulation
Abstract: Evolutionary tournaments have been used effectively as a tool for comparing game-playing algorithms. For instance, in the late 1970's, Axelrod organized tournaments to compare algorithms for playing the iterated prisoner's dilemma (PD) game. These tournaments capture the dynamics in a population of agents that periodically adopt relatively successful algorithms in the environment. While these tournaments have provided us with a better understanding of the relative merits of algorithms for iterated PD, our understanding is less clear about algorithms for playing iterated versions of arbitrary single-stage games in an environment of heterogeneous agents. While the Nash equilibrium solution concept has been used to recommend using Nash equilibrium strategies for rational players playing general-sum games, learning algorithms like fictitious play may be preferred for playing against sub-rational players. In this paper, we study the relative performance of learning and non-learning algorithms in an evolutionary tournament where agents periodically adopt relatively successful algorithms in the population. The tournament is played over a testbed composed of all possible structurally distinct 2×2 conflicted games with ordinal payoffs: a baseline, neutral testbed for comparing algorithms. Before analyzing results from the evolutionary tournament, we discuss the testbed, our choice of representative learning and non-learning algorithms and relative rankings of these algorithms in a round-robin competition. The results from the tournament highlight the advantage of learning algorithms over players using static equilibrium strategies for repeated plays of arbitrary single-stage games. The results are likely to be of more benefit compared to work on static analysis of equilibrium strategies for choosing decision procedures for open, adapting agent society consisting of a variety of competitors.
Kyle Wagner and Kerry Shaw
Journal of Artificial Societies and Social Simulation 11 (1) 3
Kyeywords: Individual-Based Model, Genetic Algorithms, Communication, Sexual Signaling, Speciation, Evolution, Genetics
Abstract: We present cricketsim, an individual-based simulator of species and community dynamics that allows experimenters to manipulate genetic and evolutionary parameters as well as parameters affecting the simulated environment and its inhabitants. The simulator can model genotypic and phenotypic features of species, such as male signals and female preferences, as well as demographic and fitness-related features. The individual-based simulator creates a lattice (cellular) world in which males and females interact by moving, signaling/responding, and mating. One or more species evolves over simulation time as individuals of a species interact with others during its lifetime, possibly creating new offspring through successful mating. The program\'s design, parameters, execution and data collection are described, an example experiment is presented, and several applications are discussed.
Journal of Artificial Societies and Social Simulation 11 (1) 8
Kyeywords: Imitation, Evolution of Cooperation, Helping Game, Indirect Reciprocity
Abstract: The relation between imitation and cooperation in evolutionary settings presents complex aspects. From one hand, in any environment where egoists are favored over cooperators by selection processes, imitation should lead to a further spreading of the former ones due to the combined processes of individual selection and replication of successful behaviors. On the other hand, if cooperators succeed in forming clusters of mutual helping individuals, imitation may have a positive effect on cooperation by further reproducing this locally dominant behavior. This paper explores the relationship between imitation and cooperation by mean of a simulation model based on two different Helping games. Our model shows that different imitation mechanisms can favor the spreading of cooperation under a wide range of conditions. Moreover, the interplay of imitation and other factors — e.g. the possibility of performing “conditional associations” strategies — can further foster the success of cooperative agents.
Oliver Will and Rainer Hegselmann
Journal of Artificial Societies and Social Simulation 11 (3) 3
Kyeywords: Replication, Social Dilemmas, Simulation Methodology, Cooperation, Trust, Agent-Based Modelling
Abstract: The article describes how and why we failed to replicate main effects of a computational model that Michael Macy and Yoshimichi Sato published in the Proceedings of the National Academy of Sciences (May 2002). The model is meant to answer a fundamental question about social life: Why, when and how is it possible to build trust with distant people? Based on their model, Macy and Sato warn the US society about an imminent danger: the possible break down of trust caused by too much social mobility. But the computational evidence for exactly that result turned out not to be replicable.
Yutaka NAKAI and Masayoshi Muto
Journal of Artificial Societies and Social Simulation 11 (3) 6
Kyeywords: Community, Carl Schmitt, a Friend and an Enemy, Tit for Tat, Coward, Evolutionary Simulation
Abstract: A society consisting of agents who can freely choose to attack or not to attack others inevitably evolves into a battling society (a \'war of all against all\'). We investigated whether strategies based on C. Schmitt\'s concept of the political, the distinction of a friend and an enemy, lead to the emergence and collapse of social order. Especially, we propose \'friend selection strategies\' (FSSs), one of which we called the \'us-TFT\' (tit for tat) strategy, which requires an agent to regard one who did not attack him or his \'friends\' as a \'friend\'. We carried out evolutionary simulations on an artificial society consisting of FSS agents. As a result, we found that the us-TFT results in a peaceful society with the emergence of an us-TFT community. In addition, we found that the collapse of a peaceful society is triggered by another FSS strategy called a \'coward\'.
Michael Macy and Yoshimichi Sato
Journal of Artificial Societies and Social Simulation 11 (4) 11
Kyeywords: Replication, Social Dilemmas, Simulation Methodology, Cooperation, Trust, Agent-Based Modelling
Abstract: [No abstract]
Thomas Grebel and Esther Merey
Journal of Artificial Societies and Social Simulation 12 (1) 12
Kyeywords: Industrial Dynamics, Neural Networks, Financial Markets, Entrepreneurship, Endogenous Evolution
Abstract: Financial markets mirror the evolution of real economic industries as much as they influence them reciprocally. In this paper we show an approach how to connect both. We will focus on the impact of industrial dynamics on financial markets. Real economic sectors as well as financial markets will be modelled using agent-based modelling techniques. Boundedly rational agents build up the endogenous evolution of an entrepreneurially driven industry, thereby substantiating the role of knowledge diffusion. Boundedly rational investors in the financial market learn about new industries and trade the corresponding shares. The complete model will be set up as a modular system which will allow investigating various scenarios.
Journal of Artificial Societies and Social Simulation 12 (1) 7
Kyeywords: Agent Based Models, Ancestor Commemoration, Dominance Relationships, Communication, Cooperation, Memory
Abstract: Many human cultures engage in the collective commemoration of dead members of their community. Ancestor veneration and other forms of commemoration may help to reduce social distance within groups, thereby encouraging reciprocity and providing a significant survival advantage. Here we present a simulation in which a prototypical form of ancestor commemoration arises spontaneously among computational agents programmed to have a small number of established human capabilities. Specifically, ancestor commemoration arises among agents that: a) form relationships with each other, b) communicate those relationships to each other, and c) undergo cycles of life and death. By demonstrating that ancestor commemoration could have arisen from the interactions of a small number of simpler behavioural patterns, this simulation may provide insight into the workings of human cultural systems, and ideas about how to study ancestor commemoration among humans.
Journal of Artificial Societies and Social Simulation 12 (1) 8
Kyeywords: Agent Based Modeling, Cooperation, Prisoners Dilemma, Spatial Interaction Model, Spatially Structured Social Dilemma, Geographic Information Systems
Abstract: The purpose of this paper is to present a spatial agent-based model of N-person prisoner's dilemma that is designed to simulate the collective communication and cooperation within a socio-geographic community. Based on a tight coupling of REPAST and a vector Geographic Information System, the model simulates the emergence of cooperation from the mobility behaviors and interaction strategies of citizen agents. To approximate human behavior, the agents are set as stochastic learning automata with Pavlovian personalities and attitudes. A review of the theory of the standard prisoner's dilemma, the iterated prisoner's dilemma, and the N-person prisoner's dilemma is given as well as an overview of the generic architecture of the agent-based model. The capabilities of the spatial N-person prisoner's dilemma component are demonstrated with several scenario simulation runs for varied initial cooperation percentages and mobility dynamics. Experimental results revealed that agent mobility and context preservation bring qualitatively different effects to the evolution of cooperative behavior in an analyzed spatial environment.
Journal of Artificial Societies and Social Simulation 12 (4) 11
Kyeywords: Replication, Social Dilemma Situations, Trust, Simulation Methodology, Cooperation
Abstract: The paper at hand aimes at identifying the assumptions that lead to the results presented in an article by Michael Macy and Yoshimichi Sato published in PNAS. In answer to a failed replication, the authors provided the source code of their model and here the results of carefully studying that code are presented. The main finding is that the simulation program implements an assumption that is most probably an unwilling, unintended, and unwanted implication of the code. This implied assumption is never mentioned in Macy and Sato's article and if the authors wanted to program what they describe in their article then it is due to a programming error. After introducing the reader to the discussion, data that stem from a new replication based on the assumptions extracted from the source code is compared with the results published in Macy and Sato's original article. The replicated results are sufficiently similar to serve as a strong indicator that this new replication implements the same relevant assumptions as the original model. Afterwards it is shown that a removal of the dubious assumption leads to results that are dramatically different from those published in Macy and Sato's PNAS article.
Tetsushi Ohdaira and Takao Terano
Journal of Artificial Societies and Social Simulation 12 (4) 7
Kyeywords: Cooperation, Altruism, Agent-Based Simulation, Evolutionary Game Theory
Abstract: In the research addressing the prisoner's dilemma game, the effectiveness and accountableness of the method allowing for the emergence of cooperation is generally discussed. The most well-known solutions for this question are memory based iteration, the tag used to distinguish between defector and cooperator, the spatial structure of the game and the either direct or indirect reciprocity. We have also challenged to approach the topic from a different point of view namely that temperate acquisitiveness in decision making could be possible to achieve cooperation. It was already shown in our previous research that the exclusion of the best decision had a remarkable effect on the emergence of an almost cooperative state. In this paper, we advance the decision of our former research to become more explainable by introducing the second-best decision. If that decision is adopted, players also reach an extremely high level cooperative state in the prisoner's dilemma game and also in that of extended strategy expression. The cooperation of this extended game is facilitated only if the product of two parameters is under the criticality. In addition, the applicability of our model to the problem in the real world is discussed.
Journal of Artificial Societies and Social Simulation 13 (1) 8
Kyeywords: Philosophy, Evolution, Selection, Standards, Epistemology, Formal Models
Abstract: There are considerable difficulties in the way of the development of useful and reliable simulation models of social phenomena, including that any simulation necessarily includes many assumptions that are not directly supported by evidence. Despite these difficulties, many still hope to develop quite general models of social phenomena. This paper argues that such hopes are ill-founded, in other words that there will be no short-cut to useful and reliable simulation models. However this paper argues that there is a way forward, that simulation modelling can be used to "boot-strap" useful knowledge about social phenomena. If each bit of simulation work can result in the rejection of some of the possible processes in observed social phenomena, even if this is about a very specific social context, then this can be used as part of a process of gradually refining our knowledge about such processes in the form of simulation models. Such a boot-strapping process will only be possible if simulation models are more carefully judged, that is a greater selective pressure is applied. In particular models which are just an analogy of social processes in computational form should be treated as "personal" rather than "scientific" knowledge. Such analogical models are useful for informing the intuition of its developers and users, but do not help the community of social simulators and social scientists to "boot-strap" reliable social knowledge. However, it is argued that both participatory modelling and evidence-based modelling can play a useful part in this process. Some kinds of simulation model are discussed with respect to their suitability for the boot-strapping of social knowledge. The knowledge that results is likely to be of a more context-specific, conditional and mundane nature than many social scientists hope for.
Journal of Artificial Societies and Social Simulation 13 (3) 2
Kyeywords: Prisoner\'s Dilemma Game, Tags, Parochial Cooperation, Clustering, Small-World-Ness, NetLogo
Abstract: Researchers from many disciplines have been interested in the maintenance of cooperation in animal and human societies using the Prisoner\'s Dilemma game. Recent studies highlight the roles of cognitively simple agents in the evolution of cooperation who read tags to interact either discriminately or selectively with tolerably similar partners. In our study on a one-shot Prisoner\'s Dilemma game, artificial agents with tags and tolerance perceive dissimilarities to local neighbors to cooperate with in-group and otherwise defect. They imitate tags and learn tolerance from more successful neighbors. In terms of efficiency, society-wide cooperation can evolve even when the benefits of cooperation are relatively low. Meanwhile, tolerance however decreases as agents become homogenized. In terms of stability, parochial cooperators are gullible to the deviants defectors displaying tolerably similar tags. We find that as the benefits of cooperation increase and the dimensions of tag space become larger, emergent societies can be more tolerant towards heterogeneous others. We also identify the effects of clustering and small-world-ness on the dynamics of tag-based parochial cooperation in spite of its fundamental vulnerability to those deviants regardless of network topology. We discuss the issue of tag changeability in search for alternative societies in which tag-based parochial cooperation is not only efficient but also robust.
Adam Zagorecki, Kilkon Ko and Louise K. Comfort
Journal of Artificial Societies and Social Simulation 13 (3) 3
Kyeywords: Agent-Based Simulation, Emergency Management, Network Evolution, Performance
Abstract: Achieving efficiency in coordinated action in rapidly changing environments has challenged both researchers and practitioners. Emergency events require both rapid response and effective coordination among participating organizations. We created a simulated operations environment using agent-based modeling to test the efficiency of six different organizational designs that varied the exercise of authority, degree of uncertainty, and access to information. Efficiency is measured in terms of response time, identifying time as the most valuable resource in emergency response. Our findings show that, contrary to dominant organizational patterns of hierarchical authority that limit communication among members via strict reporting rules, any communication among members increases the efficiency of organizations operating in uncertain environments. We further found that a smaller component of highly interconnected, self adapting agents emerges over time to support the organization\'s adaptation in changing conditions. In uncertain environments, heterogeneous agents prove more efficient in sharing information that guides coordination than homogeneous agents.
Klaus Jaffe and Luis Zaballa
Journal of Artificial Societies and Social Simulation 13 (3) 4
Kyeywords: Altruism, Cooperation, Social, Prosocial, Cohesion, Evolution, Punishment, Retribution
Abstract: Most current attempts to explain the evolution - through individual selection - of pro-social behavior (i.e. behavior that favors the group) that allows for cohesive societies among non related individuals, focus on altruistic punishment as its evolutionary driving force. The main theoretical problem facing this line of research is that in the exercise of altruistic punishment the benefits of punishment are enjoyed collectively while its costs are borne individually. We propose that social cohesion might be achieved by a form of punishment, widely practiced among humans and animals forming bands and engaging in mob beatings, which we call co-operative punishment. This kind of punishment is contingent upon - not independent from - the concurrent participation of other actors. Its costs can be divided among group members in the same way as its benefits are, and it will be favoured by evolution as long as the benefits exceed the costs. We show with computer simulations that co-operative punishment is an evolutionary stable strategy that performs better in evolutionary terms than non-cooperative punishment, and demonstrate the evolvability and sustainability of pro-social behavior in an environment where not necessarily all individuals participate in co-operative punishment. Co-operative punishment together with pro-social behavior produces a self reinforcing system that allows the emergence of a 'Darwinian Leviathan' that strengthens social institutions.
Jae-Woo Kim and Robert Hanneman
Journal of Artificial Societies and Social Simulation 14 (3) 1
Kyeywords: Workers Protest, Tags, Group Identity, Trust, Netlogo
Abstract: This paper presents an agent-based model of worker protest. Workers have varying degrees of grievance depending on the difference between their wage and the average of their neighbors. They protest with probabilities proportional to grievance, but are inhibited by the risk of being arrested – which is determined by the ratio of coercive agents to probable rebels in the local area. We explore the effect of similarity perception on the dynamics of collective behavior. If workers are surrounded by more in-group members, they are more risk-taking; if surrounded by more out-group members, more risk-averse. Individual interest and group membership jointly affect patterns of workers protest: rhythm, frequency, strength, and duration of protest outbreaks. Results indicate that when wages are more unequally distributed, the previous outburst tends to suppress the next one, protests occur more frequently, and they become more intensive and persistent. Group identification does not seriously influence the frequency of local uprisings. Both their strength and duration, however, are negatively affected by the ingroup-outgroup assessment. The overall findings are valid when workers distinguish \'us\' from \'them\' through simple binary categorization, as well as when they perceive degrees of similarity and difference from their neighbors.
Tetsushi Ohdaira and Takao Terano
Journal of Artificial Societies and Social Simulation 14 (3) 3
Kyeywords: Cooperation, Second-Best Decision, Multi-Agent Simulation, Spatial Game, Collusive Tendering
Abstract: Recently, the area of study of spatial game continuously has extended, and researchers have especially presented a lot of works of coevolutionary mechanism. We have recognized coevolutionary mechanism as one of the factors for the promotion of cooperation like five rules by Nowak. However, those studies still deal with the optimal response (best decision). The best decision is persuasive in most cases, but does not apply to all situations in the real world. Contemplating that question, researchers have presented some works discussing not only the best decision but also the second-best decision. Those studies compare the results between the best and the second-best, and also state the applicability of the second-best decision. This study, considering that trend, has extended the match between two groups to spatial game with the second-best decision. This extended model expresses relationships of groups as a spatial network, and every group matches other groups of relationships. Then, we examine how mutual cooperation changes in each case where either we add probabilistic perturbation to relationships or ties form various types of the structure. As a result, unlike most results utilizing the best decision, probabilistic perturbation does not induce any change. On the other hand, when ties are the scale-free structure, mutual cooperation is enhanced like the case of the best decision. When we probe the evolution of strategies in that case, groups with many ties play a role for leading the direction of decision as a whole. This role appears without explicit assignment. In the discussion, we also state that the presented model has an analogy to the real situation, collusive tendering.
Journal of Artificial Societies and Social Simulation 14 (4) 1
Kyeywords: Simulating Science, Algorithmic Chemistry, Evolutionary Algorithms, Data Structures, Learning Systems
Abstract: This note discusses two challenges to simulating the social process of science. The first is developing an adequately rich representation of the underlying Data Generation Process which scientific progress can \"learn\". The second is how to get effective data on what, in broad terms, the properties of the \"future\" are. Paradoxically, with due care, we may learn a lot about the future by studying the past.
Warren Thorngate, Jing Liu and Wahida Chowdhury
Journal of Artificial Societies and Social Simulation 14 (4) 17
Kyeywords: Attention, Competition, Evolution, Information, Production, Consumption
Abstract: Whenever the amount of information produced exceeds the amount of attention available to consume it, a competition for attention is born. The competition is increasingly fierce in science where the exponential growth of information has forced its producers, consumers and gatekeepers to become increasingly selective in what they attend to and what they ignore. Paradoxically, as the criteria of selection among authors, editors and readers of scientific journal articles co-evolve, they show signs of becoming increasingly unscientific. The present article suggests how the paradox can be addressed with computer simulation, and what its implications for the future of science might be.
Hang Ye, Fei Tan, Mei Ding, Yongmin Jia and Yefeng Chen
Journal of Artificial Societies and Social Simulation 14 (4) 20
Kyeywords: Public Goods Game, Cooperation, Social Dilemma, Co-Evolution, Sympathy, Punishment
Abstract: An important way to maintain human cooperation is punishing defection. However, since punishment is costly, how can it arise and evolve given that individuals who contribute but do not punish fare better than the punishers? This leads to a violation of causality, since the evolution of punishment is prior to the one of cooperation behaviour in evolutionary dynamics. Our public goods game computer simulations based on generalized Moran Process, show that, if there exists a \'behaviour-based sympathy\' that compensates those who punish at a personal cost, the way for the emergence and establishment of punishing behaviour is paved. In this way, the causality violation dissipates. Among humans sympathy can be expressed in many ways such as care, praise, solace, ethical support, admiration, and sometimes even adoration; in our computer simulations, we use a small amount of transfer payment to express \'behaviour-based sympathy\'. Our conclusions indicate that, there exists co-evolution of sympathy, punishment and cooperation. According to classical philosophy literature, sympathy is a key factor in morality and justice is embodied by punishment; in modern societies, both the moral norms and the judicial system, the representations of sympathy and punishment, play an essential role in stable social cooperation.
Journal of Artificial Societies and Social Simulation 14 (4) 7
Kyeywords: Philosophy, Science, Simulation, Social Processes, Evolutionary Models, Sociology
Abstract: This briefly reviews some philosophy of science that might be relevant to simulating the social processes of science. It also includes a couple of examples from the sociology of science because these are inextricable from the philosophy.
Journal of Artificial Societies and Social Simulation 14 (4) 9
Kyeywords: Agent-Based Models, Science Dynamics, Social Networks, Scientometrics, Evolutionary Computation
Abstract: The goal of this paper is to provide a sketch of what an agent-based model of the scientific process could be. It is argued that such a model should be constructed with normative claims in mind: i.e. that it should be useful for scientific policy making. In our tentative model, agents are researchers producing ideas that are points on an epistemic landscape. We are interested in our agents finding the best possible ideas. Our agents are interested in acquiring credit from their peers, which they can do by writing papers that are going to get cited by other scientists. They can also share their ideas with collaborators and students, which will help them eventually get cited. The model is designed to answer questions about the effect that different possible behaviors have on both the individual scientists and the scientific community as a whole.
Journal of Artificial Societies and Social Simulation 15 (1) 7
Kyeywords: R&D Subsidies, Rivalry Versus Cooperation, Dynamic-Stochastic Games, Simulations
Abstract: By means of a simulated funding-agency/supported-firm stochastic dynamic game, this paper shows that the level of the subsidy provided by a funding (public) agency, normally used to correct for firm R&D shortage, might be severely underprovided. This is due to the "externalities" generated by the agency-firm strategic relationship, as showed by comparing two versions of the model: one assuming "rival" behaviors between companies and agency (i.e., the current setting), and one associated to the "cooperative" strategy (i.e. the optimal Pareto-efficient benchmark). The paper looks also at what "welfare" implications are associated to different degrees of persistency in the funding effect on corporate R&D. Three main conclusions are thus drawn: (i) the relative quota of the subsidy to R&D is undersized in the rival compared to the cooperative model; (ii) the rivalry strategy generates distortions that favor the agency compared to firms; (iii) when passing from less persistent to more persistent R&D additionality/crowding-out effect, the lower the distortion the greater the variance is and vice versa. As for the management of R&D funding policies, we suggest that all the elements favouring greater collaboration between agency and firm objectives may help current R&D support to approach its social optimum.
Alberto Acerbi, Stefano Ghirlanda and Magnus Enquist
Journal of Artificial Societies and Social Simulation 15 (4) 1
Kyeywords: Cultural Evolution, Cultural Transmission, Cultural Change, Age, Age-Biased Transmission, Openness
Abstract: We explore the impact of age on cultural change through simulations of cultural evolution. Our simulations show that common observations about the relationship between old and young naturally emerge from repeated cultural learning. In particular, young individuals are more open to learn than older individuals, they are less effective as cultural models, and they possess less cultural traits. We also show that, being more open to learning, young individuals are an important source of cultural change. Cultural change, however, is faster in populations with both young and old. A relatively large share of older individuals, in fact, allows a population to retain more culture, and a large culture can change in more directions than a small culture. For the same reason, considering age-biased cultural transmission in an overlapping generations model, cultural evolution is slower when individuals interact preferentially with models of similar age than when they mainly interact with older models.
Alistair Sutcliffe, Di Wang and Robin Dunbar
Journal of Artificial Societies and Social Simulation 15 (4) 3
Kyeywords: Social Agents, Social Relationships, Trust, Evolution, Social Straegies
Abstract: In complex social systems such as those of many mammals, including humans, groups (and hence ego-centric social networks) are commonly structured in discrete layers. We describe a computational model for the development of social relationships based on agents' strategies for social interaction that favour more less-intense, or fewer more-intense partners. A trust-related process controls the formation and decay of relationships as a function of interaction frequency, the history of interaction, and the agents' strategies. A good fit of the observed layers of human social networks was found across a range of model parameter settings. Social interaction strategies which favour interacting with existing strong ties or a time-variant strategy produced more observation-conformant results than strategies favouring more weak relationships. Strong-tie strategies spread in populations under a range of fitness conditions favouring wellbeing, whereas weak-tie strategies spread when fitness favours foraging for food. The implications for modelling the emergence of social relationships in complex structured social networks are discussed.
Shade T. Shutters and David Hales
Journal of Artificial Societies and Social Simulation 16 (1) 4
Kyeywords: Cooperation, Evolution, Green Beard, Social Parasitism, Chromodynamics
Abstract: Cooperation is essential for complex biological and social systems and explaining its evolutionary origins remains a central question in several disciplines. Tag systems are a class of models demonstrating the evolution of cooperation between selfish replicators. A number of previous models have been presented but they have not been widely explored. Though previous researchers have concentrated on the effects of one or several parameters of tag models, exploring exactly what is meant by cheating in a tag system has received little attention. Here we re-implement three previous models of tag-mediated altruism and introduce four definitions of cheaters. Previous models have used what we consider weaker versions of cheaters that may not exploit cooperators to the degree possible, or to the degree observed in natural systems. We find that the level of altruism that evolves in a population is highly contingent on how cheaters are defined. In particular when cheaters are defined as agents that display an appropriate tag but have no mechanism for participating in altruistic acts themselves, a population is quickly invaded by cheaters and all altruism collapses. Even in the intermediate case where cheaters may revert back to a tag-tolerance mode of interaction, only minimal levels of altruism evolve. Our results suggest that models of tag-mediated altruism using stronger types of cheaters may require additional mechanisms, such as punishment strategies or multi-level selection, to evolve meaningful levels of altruism.
Journal of Artificial Societies and Social Simulation 16 (2) 6
Kyeywords: Evolution of Cooperation, Complex Network, Spatial Game, Conditional Cooperation
Abstract: The investigation of how cooperation is achieved on graphs in the field of spatial game or network reciprocity has received proliferating attention in the biological and sociological literature. In line of the research, this paper provides an new account of how cooperation could evolve in complex networks when actors use information of network characteristics to strategize whether to cooperate or not. Different from past work that focuses exclusively on the evolution of unconditional cooperation, we are proposing new strategies that are choosy in whom to cooperate with, conditional on the structural attributes of the nodes occupied by actors. In a series of evolutionary tournaments conducted by computer simulation, the model shows that a pair of simple strategies-cooperating respectively with higher and lower nodal-attribute neighbors-can be advantageous in adaptive fitness when competing against unconditional cooperation and defection. In particular, these strategies of conditional cooperation work well in random graphs-a network known for being unfavorable to the selection of cooperation. This paper contributes to the literature by showing how network characteristics can serve as a mechanism to sustain cooperation in some hostile network environments where unconditional cooperation is unable to evolve. The cognitive foundations of the mechanism and its implications are discussed.
Max Hartshorn, Artem Kaznatcheev and Thomas Shultz
Journal of Artificial Societies and Social Simulation 16 (3) 7
Kyeywords: Ethnocentrism, Evolution of Cooperation, Evolutionary Game Theory, Minimal Cognition, Prisoner's Dilemma
Abstract: Recent agent-based computer simulations suggest that ethnocentrism, often thought to rely on complex social cognition and learning, may have arisen through biological evolution. From a random start, ethnocentric strategies dominate other possible strategies (selfish, traitorous, and humanitarian) based on cooperation or non-cooperation with in-group and out-group agents. Here we show that ethnocentrism eventually overcomes its closest competitor, humanitarianism, by exploiting humanitarian cooperation across group boundaries as world population saturates. Selfish and traitorous strategies are self-limiting because such agents do not cooperate with agents sharing the same genes. Traitorous strategies fare even worse than selfish ones because traitors are exploited by ethnocentrics across group boundaries in the same manner as humanitarians are, via unreciprocated cooperation. By tracking evolution across time, we find individual differences between evolving worlds in terms of early humanitarian competition with ethnocentrism, including early stages of humanitarian dominance. Our evidence indicates that such variation, in terms of differences between humanitarian and ethnocentric agents, is normally distributed and due to early, rather than later, stochastic differences in immigrant strategies.
Benjamin D. Nye
Journal of Artificial Societies and Social Simulation 16 (4) 2
Kyeywords: Evolution, Acquired Resistance, Agent Based Modeling, Selection Pressure, SIS Model, PS-I
Abstract: The proliferation of resistant strains has been an unintended side effect of human interventions designed to eliminate unwanted elements of our environment. Any attempt to destroy an adaptive population must also be considered as a selection pressure, so that the most resistant members will comprise the next generation. Procedures have been developed to slow the evolution of resistances in a population, with the most common approaches being overkill and treatment cycling. This paper presents an agent-based Susceptible-Infection-Susceptible (SIS) model to explore the effectiveness of these procedures on an abstract epidemic of pathogens, focusing on how the interaction between interventions and mutations affects acquired resistance. Illustrative findings indicate that overkill performed better than cycling treatments when variation in resistances had a high degree of heritability. When resistance variation was effectively memoryless, cycling and overkill performed comparably. However, overkill was prone to backlash outliers where an amplification of infection resistance occurred- a significant drawback to the overkill technique. These backlash events indicate that cycling interventions might be more effective when variation is memoryless and carrying resistance incurs a cost to overall fitness. However, under limited fitness-cost conditions explored, cycling performed no better than overkill for preventing resistance.
J. Kasmire, Igor Nikolic and Gerard Dijkema
Journal of Artificial Societies and Social Simulation 16 (4) 7
Kyeywords: Universal Darwinism, Complex Adaptive Systems, Evolution, Greenhouse Horticulture, Innovation
Abstract: To explore the space between the theories of the Diffusion of Innovations and Universal Darwinism, we first examine a case study of the history of the greenhouse horticulture sector of the Netherlands, comparing and contrasting the narrow focus of Diffusion of Innovations and the wider focus of Universal Darwinism. We then build an agent-based model using elements of both in order to test how well the Diffusion of Innovations theory holds up when some of its simplifications are removed. Results show that the single, simple pattern prominent in Diffusions of Innovations theory does emerge, but that it is only one of several patterns and that it does not behave precisely as expected. Results also show agent properties, such as stubbornness or innovativeness, can be surprisingly complex, as when stubbornness shows an advantage in the long term, while innovativeness was beneficial to the network but not to the innovator. While the Diffusion of Innovations theory is simple and can easily guide policy decisions, this paper shows that adding complexity to place diffusions inside a larger evolutionary context results in more realistic analysis and can help policy-makers to achieve challenging goals amidst modern economic and political challenges.
Journal of Artificial Societies and Social Simulation 17 (3) 3
Kyeywords: Evolution, Economics, Fitness Test, Evolutionary System, Aggregation
Abstract: If local circumstances can generate local social trends, it follows that global circumstances can generate global trends. Furthermore, modern global circumstances match the conditions used to create artificial evolutionary systems. If it is possible for evolutionary forces to arise in global society, then it is possible that key forces shaping global society are evolutionary in nature. We can experimentally test for the possibility of evolutionary forces in global society by using a multi-agent simulation. This paper presents a simulation programmed to capture the evolutionary prerequisites observed in global society. Trends arising from this simulation are tested against three known trends and three assumed trends arising from global society. The results from this experiment support the hypothesis that a wealth aggregation evolutionary imperative is shaping key trends in global society.
Cara H. Kahl and Hans Hansen
Journal of Artificial Societies and Social Simulation 18 (1) 4
Kyeywords: Creativity, Social Psychology, Mihaly Csikszentmihalyi, Social Systems, Cultural Evolution, Information Theory
Abstract: Psychological research on human creativity focuses primarily on individual creative performance. Assessing creative performance is, however, also a matter of expert evaluation. Few psychological studies model this aspect explicitly as a human process, let alone measure creativity longitudinally. An agent-based model was built to explore the effects contextual factors such as evaluation and temporality have on creativity. Mihaly Csikszentmihalyi’s systems perspective of creativity is used as the model’s framework, and stylized facts from the domain of creativity research in psychology provide the model’s contents. Theoretical experimentation with the model indicated evaluators and their selection criteria play a bearing role in constructing human creativity. This insight has major implications for designing future creativity research in psychology.
Mike Farjam, Marco Faillo, Ida Sprinkhuizen-Kuyper and Pim Haselager
Journal of Artificial Societies and Social Simulation 18 (1) 5
Kyeywords: Public Goods Games, Punishment, Cooperation, Reciprocity, Evolution of Cooperation
Abstract: In social dilemmas punishment costs resources, not just from the one who is punished but often also from the punisher and society. Reciprocity on the other side is known to lead to cooperation without the costs of punishment. The questions at hand are whether punishment brings advantages besides its costs, and how its negative side-effects can be reduced to a minimum in an environment populated by agents adopting a form of reciprocity. Various punishment mechanisms have been studied in the economic literature such as unrestricted punishment, legitimate punishment, cooperative punishment, and the hired gun mechanism. In this study all these mechanisms are implemented in a simulation where agents can share resources and may decide to punish other agents when the other agents do not share. Through evolutionary learning agents adapt their sharing/punishing policy. When the availability of resources was restricted, punishment mechanisms in general performed better than no-punishment, although unrestricted punishment was performing worse. When resource availability was high, performance was better in no-punishment conditions with indirect reciprocity. Unrestricted punishment was always the worst performing mechanism. Summarized, this paper shows that, in certain environments, some punishment mechanisms can improve the efficiency of cooperation even if the cooperating system is already based on indirect reciprocity.
Giulio Cimini and Angel Sanchez
Journal of Artificial Societies and Social Simulation 18 (2) 22
Kyeywords: Evolutionary Game Theory, Prisoner's Dilemma, Network Reciprocity
Abstract: Cooperation lies at the foundations of human societies, yet why people cooperate remains a conundrum. The issue, known as network reciprocity, of whether population structure can foster cooperative behavior in social dilemmas has been addressed by many, but theoretical studies have yielded contradictory results so far—as the problem is very sensitive to how players adapt their strategy. However, recent experiments with the prisoner’s dilemma game played on different networks and in a specific range of payoffs suggest that humans, at least for those experimental setups, do not consider neighbors’ payoffs when making their decisions, and that the network structure does not influence the final outcome. In this work we carry out an extensive analysis of different evolutionary dynamics, taking into account most of the alternatives that have been proposed so far to implement players’ strategy updating process. In this manner we show that the absence of network reciprocity is a general feature of the dynamics (among those we consider) that do not take neighbors’ payoffs into account. Our results, together with experimental evidence, hint at how to properly model real people’s behavior.
Shade T. Shutters and David Hales
Journal of Artificial Societies and Social Simulation 18 (3) 2
Kyeywords: Tags, Thresholds, Altruism, Evolution, Cooperation, Social Harmonics
Abstract: Several parameters combine to govern the nature of agent interactions in evolutionary social simulations. Previous work has suggested that these parameters may have complex interplay that is obscured when they are not analyzed separately. Here we focus specifically on how three population-level parameters, which govern agent interactions, affect levels of altruism in a population. Specifically we vary how frequently agents interact in a generation, how far along a network they may interact, and the size of the population. We show that the frequency with which an agent interacts with its neighbors during a generation has a strong effect on levels of evolved altruism – provided that those pairings are stochastic. When agents interact equally with all of their neighbors, regardless of how often, minimal levels of altruism evolve. We further report a curious harmonic signature in the level of altruism resulting from the interplay of the benefit-cost ratio of an altruistic act and the number of agent interactions per generation. While the level of altruism is generally an increasing function of the number of pairings per generation, at each instance where pairings equals a multiple of the benefit-cost ratio a sharp discontinuity occurs, precipitating a drop onto a lower-value function. We explore the nature of these discontinuities by examining the temporal dynamics and spatial configuration of agents. Finally, we show that rules for the evolution of cooperation that are based on network density may be inadvertently missing effects that are due to the frequency of interactions and whether those interactions are symmetrical among neighbors.
Nicholas Seltzer and Oleg Smirnov
Journal of Artificial Societies and Social Simulation 18 (4) 12
Kyeywords: Cooperation, Social Networks, Small-World, Modern Society, Simulation, Agent-Based
Abstract: We analyze a novel agent-based model of a social network in which agents make contributions to others conditional upon the social distance, which we measure in terms of the “degrees of separation” between the two players. On the basis of a simple imitation model, the emerging strategy profile is characterized by high levels of cooperation with those who are directly connected to the agent and lower but positive levels of cooperation with those who are indirectly connected to the agent. Increasing maximum interaction distance decreases cooperation with close neighbors but increases cooperation with distant neighbors for a net negative effect. On the other hand, allowing agents to learn and imitate socially distant neighbors increases cooperation for all types of interaction. Combining greater interaction distance with greater learning distance leads to a positive change in the total social welfare produced by the agents’ contributions.
Journal of Artificial Societies and Social Simulation 18 (4) 14
Kyeywords: Social Norms, Agent-Based Modeling, Social Networks, Neighborhood Structure, Cooperation
Abstract: The different ways individuals socialize with others affect the conditions under which social norms are able to emerge. In this work an agent-based model of cooperation in a population of adaptive agents is presented. The model has the ability to implement a multitude of network topologies. The agents possess strategies represented by boldness and vengefulness values in the spirit of Axelrod's (1986) norms game. However, unlike in the norms game, the simulations abandon the evolutionary approach and only follow a single-generation of agents who are nevertheless able to adapt their strategies based on changes in their environment. The model is analyzed for potential emergence or collapse of norms under different network and neighborhood configurations as well as different vigilance levels in the agent population. In doing so the model is found able to exhibit interesting emergent behavior suggesting potential for norm establishment even without the use of so-called metanorms. Although the model shows that the success of the norm is dependent on the neighborhood size and the vigilance of the agent population, the likelihood of norm collapse is not monotonically related to decreases in vigilance.
Genki Ichinose, Masaya Saito, Hiroki Sayama and Hugues Bersini
Journal of Artificial Societies and Social Simulation 18 (4) 3
Kyeywords: Evolution of Cooperation, Tag, Spatial Structure, Migration, Segregation
Abstract: Cooperation is ubiquitous in biological and social systems. Previous studies revealed that a preference toward similar appearance promotes cooperation, a phenomenon called tag-mediated cooperation or communitarian cooperation. This effect is enhanced when a spatial structure is incorporated, because space allows agents sharing an identical tag to regroup to form locally cooperative clusters. In spatially distributed settings, one can also consider migration of organisms, which has a potential to further promote evolution of cooperation by facilitating spatial clustering. However, it has not yet been considered in spatial tag-mediated cooperation models. Here we show, using computer simulations of a spatial model of evolutionary games with organismal migration, that tag-based segregation and homophilic cooperation arise for a wide range of parameters. In the meantime, our results also show another evolutionarily stable outcome, where a high level of heterophilic cooperation is maintained in spatially well-mixed patterns. We found that these two different forms of tag-mediated cooperation appear alternately as the parameter for temptation to defect is increased.
Journal of Artificial Societies and Social Simulation 19 (2) 4
Kyeywords: Evolution, Cooperation, Reciprocity
Abstract: Many models of the evolution of cooperation have shown the importance of direct reciprocity (for example “tit for tat” strategies) or alternatively indirect reciprocity (conspicuous altruism based on a reputation or “image score”). In the latter case many models make the implicit assumption that group sizes are large relative to the expected number of interactions, which makes their analysis more tractable in several ways, not least by allowing us to ignore any strategic interaction between the direct and indirect classes of reciprocation strategy. However, in smaller groups the possibility arises that both classes of strategy will play a role in determining the equilibrium behaviour. Therefore we introduce a replicator dynamics model which incorporates both direct and indirect reciprocity, and use simulation and numerical methods to quantitatively assess how the level of cooperation in equilibrium is affected by changes in the group size and the frequency with which other group members are encountered. Our analysis shows that, for intermediate group sizes, direct reciprocity persists in equilibrium alongside indirect reciprocity. In contrast to previous simulation studies, we provide a sound game-theoretic underpinning to our analysis, and examine the precise conditions which give rise to a mix of both forms of reciprocity.
Mark J. O. Bagley
Journal of Artificial Societies and Social Simulation 20 (3) 9
Kyeywords: Industrial Clusters, Spin-Offs, Schumpeter, Evolutionary Economic Geography, Technological Change
Abstract: This paper describes how patterns of industrial clustering arise with respect to the size of an initial firm when measured in terms of innovation. Through principles of evolutionary economics, the aim of this paper is to examine the ‘birth’ of industrial clusters. We take an endogenous and supply-side approach, where firms in a region spawn from incumbents. Technology is qualitatively described using a code set mapped on a cognitive space. Assuming inheritability of networking skills, we seek to model how the size of an initial firm influences future patterns of cluster formation through a model of technical cognition and a mimicking of creativity. It is found that initial firm size has a lasting impact on clustering patterns through its influence on the level of cognitive distance of the underlying agents. The model replicates the stylised facts of entrepreneurial cluster formation.
Piotr Mateusz Patrzyk and Martin Takáč
Journal of Artificial Societies and Social Simulation 20 (4) 5
Kyeywords: Cooperation, Punishment, Revenge, Conflict, Aggression, Morality
Abstract: Can human aggressiveness promote peaceful cooperation? Despite the seeming contradiction of these phenomena, our study suggests the answer is yes. We develop two agent-based models of cooperative interactions among aggressive agents threatening each other. In Model 1, we show that aggressive displays performed by dominance-seeking individuals create a system of mutual threats that effectively enforces cooperation and inhibits agents from escalating conflicts. This happens because agents observe each other fighting, which deters them from attacking each other due to aggressive reputations. In Model 2 we extend this effect to third-party interventions showing that forming alliances makes attacks more efficient and promotes the emergence of common rules determining whom to fight against. In such a state, social order is maintained by the existence of moral alliances – groups of agents willing to fight against norm violators. In summary, we argue that reputation for toughness and the aggressive predisposition of humans could have played an important role in the evolution of cooperation and moral systems.
Journal of Artificial Societies and Social Simulation 21 (1) 11
Kyeywords: Inequality, Dynamical Regimes, Lattice, Transitions, Tags
Abstract: We discuss a model of inequity based on iteration of the Nash multi-agent bargaining game on a lattice. Agent's choices are based on a logit function and gradual decay of memories of past profits. Numerical simulations demonstrate the stability of various dynamical regimes, such as disorder, fairness or inequity, according to parameters and initial conditions. When playing the game on a lattice i.e. using neighbouring agent interactions instead of random interaction among the whole agent population, one observes spatial domains and specific patterns in addition to the temporal convergence toward attractors observed when interactions involve any pair of agents. A result specific to the network topology is the co-existence of domains with different regimes, allowing the emergence of the inequity condition even in the absence of tags.
Engi Amin, Mohamed Abouelela and Amal Soliman
Journal of Artificial Societies and Social Simulation 21 (1) 3
Kyeywords: Agent-Based Simulation, Cooperation, Public Goods Game, Laboratory Experiment, Social Preferences
Abstract: This paper examines the role of heterogeneous agents in the study of voluntary contributions to public goods. A human-subject experiment was conducted to classify agent types and determine their effects on contribution levels. Data from the experiment was used to build and calibrate an agent-based simulation model. The simulations display how different compositions of agent preference types affect the contribution levels. Findings indicate that the heterogeneity of cooperative preferences is an important determinant of a population’s contribution pattern.
Ismo T. Koponen and Maija Nousiainen
Journal of Artificial Societies and Social Simulation 21 (2) 1
Kyeywords: Discourse Patterns, Task Focused Groups, Agent-Based Model, Competition, Cooperation
Abstract: Discourse patterns in a small group are assumed to form largely through the group's internal social dynamics when group members compete for floor in discourse. Here we approach such discourse pattern formation through the agent-based model (ABM). In the ABM introduced here the agents' interactions and participation in discussions are dependent on the agents' inherent potential activity to participate in discussion and on realised, externalised activity, discursivity. The discourse patterns are assumed to be outcomes of peer-to-peer comparison events, where agents competitively compare their activities and discursivities, and where activities also affect agents' cooperation in increasing the discursivity, i.e. floor for discourse. These two effects and their influence on discourse pattern formation are parameterised as comptetivity and cooperativity. The discourse patterns are here based on the agents' discursivity. The patterns in groups of four agents up to seven agents are characterised through triadic census (i.e. though counting triadic sub-patterns). The cases of low competitivity is shown to give rise to fully connected egalitarian, triadic patterns, which with increasing competitivity are transformed to strong dyadic patterns. An increase in cooperativity enhances the emergence of egalitarian triads and helps to maintain the formation of fully and partially connected triadic pattern also in cases of high competitivity. In larger groups of six and seven agents, isolation becomes common, in contrast to groups of four agents where isolation is relatively rare. These results are in concordance with known empirical findings of discourse and participation patterns in small groups.
Journal of Artificial Societies and Social Simulation 21 (3) 7
Kyeywords: Agent-Based Models, Gender Inequalities, Career Preferences, Social Learning, Evolution
Abstract: An agent-based simulation framework is presented that provides a principled approach for investigating gender inequalities in professional hierarchies such as universities or businesses. Populations of artificial agents compete for promotion in their chosen professions, leading to emergent distributions that can be matched to real-life scenarios, and allowing the influence of socially or genetically acquired career preferences to be explored. The aim is that such models will enable better understanding of how imbalances emerge and evolve, facilitate the identification of specific signals that can indicate the presence or absence of discrimination, and provide a tool for determining how and when particular intervention strategies may be appropriate for rectifying any inequalities. Results generated from a representative series of abstract case studies involving innate or culturally-acquired gender-based ability differences, gender-based discrimination, and various forms of gender-specific career preferences, demonstrate the power of the approach. These simulations will hopefully inspire and facilitate better approaches for dealing with these issues in real life.
Morgane Dumont, Johan Barthelemy, Nam Huynh and Timoteo Carletti
Journal of Artificial Societies and Social Simulation 21 (4) 3
Kyeywords: Microsimulation, Agent-Based Modelling, Ordering of Models, Population Evolution, Robustness
Abstract: Agent based modelling is nowadays widely used in transport and the social science. Forecasting population evolution and analysing the impact of hypothetical policies are often the main goal of these developments. Such models are based on sub-models defining the interactions of agents either with other agents or with their environment. Sometimes, several models represent phenomena arising at the same time in the real life. Hence, the question of the order in which these sub-models need to be applied is very relevant for simulation outcomes. This paper aims to analyse and quantify the impact of the change in the order of sub-models on an evolving population modelled using TransMob. This software simulates the evolution of the population of a metropolitan area in South East of Sydney (Australia). It includes five principal models: ageing, death, birth, marriage and divorce. Each possible order implies slightly different results mainly driven by how agents' ageing is defined with respect to death. Furthermore, we present a calendar-based approach for the ordering that decreases the variability of final populations. Finally, guidelines are provided proposing general advices and recommendations for researchers designing discrete time agent-based models.
Azhar Mohd Ibrahim, Ibrahim Venkat and Philippe De Wilde
Journal of Artificial Societies and Social Simulation 22 (1) 3
Kyeywords: Evacuation Model, Evolution of Crowd Behaviour, Crowd Disaster, Evolutionary Game Theory
Abstract: Crowd dynamics have important applications in evacuation management systems relevant to organizing safer large scale gatherings. For crowd safety, it is very important to study the evolution of potential crowd behaviours by simulating the crowd evacuation process. Planning crowd control tasks by studying the impact of crowd behaviour evolution towards evacuation could mitigate the possibility of crowd disasters. During a typical emergency evacuation scenario, conflict among agents occurs when agents intend to move to the same location as a result of the interaction with their nearest neighbours. The effect of the agent response towards their neighbourhood is vital in order to understand the effect of variation of crowd behaviour on the whole environment. In this work, we model crowd motion subject to exit congestion under uncertainty conditions in a continuous space via computer simulations. We model best-response, risk-seeking, risk-averse and risk-neutral behaviours of agents via certain game-theoretic notions. We perform computer simulations with heterogeneous populations in order to study the effect of the evolution of agent behaviours towards egress flow under threat conditions. Our simulation results show the relation between the local crowd pressure and the number of injured agents. We observe that when the proportion of agents in a population of risk-seeking agents is increased, the average crowd pressure, average local density and the number of injured agents increases. Besides that, based on our simulation results, we can infer that crowd disasters could be prevented if the agent population consists entirely of risk-averse and risk-neutral agents despite circumstances that lead to threats.
Haijun Bao, Xiaohe Wu, Haowen Wang, Qiuxiang Li, Yi Peng and Shibao Lu
Journal of Artificial Societies and Social Simulation 22 (1) 7
Kyeywords: Conflict of Interests, Land Expropriation, Evolutionary Game, Multi-Agent Simulation, Farmers
Abstract: Expropriation of collectively-owned land has become an important realistic path for achieving urban development and new urbanization in China considering the shortage of state-owned land. During this process, farmers involved in land expropriation are often in conflict with one another because of the asymmetry of their interests. Such conflicts have a considerable effect on social harmony and stability. However, few studies have investigated such conflict of interests between farmers. Therefore, this research analyzed game behavior for the conflict of interests among farmers. A two-dimensional symmetric evolutionary game model and a multi-agent simulation experiment were used to explore the conflicts induced by the farmers’ different responses to land expropriation. This research finds that the changing strategy choices of farmers in the evolutionary game on collectively owned land expropriation are the main reasons for the occurrence of villager’ confrontations and “nail households”. Results provide targeted policy recommendations for local governments to promote cooperation among farmers, thereby enhancing social harmony. The findings also serve as references for other countries and regions in dealing with intra-conflict of interests in land expropriation.
Journal of Artificial Societies and Social Simulation 22 (2) 6
Kyeywords: Evolution, Cooperation, Inter-Group, Conflict, Warfare, Tribal
Abstract: This study investigates a possible nexus between inter-group competition and intra-group cooperation, which may be called "tribalism." Building upon previous studies demonstrating a relationship between the environment and social relations, the present research incorporates a social-ecological model as a mediating factor connecting both individuals and communities to the environment. Cyclical and non-cyclical fluctuation in a simple, two-resource ecology drive agents to adopt either "go-it-alone" or group-based survival strategies via evolutionary selection. Novelly, this simulation employs a multilevel selection model allowing group-level dynamics to exert downward selective pressures on individuals' propensity to cooperate within groups. Results suggest that cooperation and inter-group conflict are co-evolved in a triadic relationship with the environment. Resource scarcity increases inter-group competition, especially when resources are clustered as opposed to widely distributed. Moreover, the tactical advantage of cooperation in the securing of clustered resources enhanced selective pressure on cooperation, even if that implies increased individual mortality for the most altruistic warriors. Troubling, these results suggest that extreme weather, possibly as a result of climate change, could exacerbate conflict in sensitive, weather-dependent social-ecologies---especially places like the Horn of Africa where ecologically sensitive economic modalities overlap with high-levels of diversity and the wide-availability of small arms. As well, global development and foreign aid strategists should consider how plans may increase the value of particular locations where community resources are built or aid is distributed, potentially instigating tribal conflict. In sum, these factors, interacting with pre-existing social dynamics dynamics, may heighten inter-ethnic or tribal conflict in pluralistic but otherwise peaceful communities.
Serge Wiltshire, Asim Zia, Christopher Koliba, Gabriela Bucini, Eric Clark, Scott Merrill, Julie Smith and Susan Moegenburg
Journal of Artificial Societies and Social Simulation 22 (2) 8
Kyeywords: Agent-Based Modeling, Network Analytics, Computational Epidemiology, Evolutionary Computation, Livestock Production
Abstract: We developed an agent-based susceptible/infective model which simulates disease incursions in the hog production chain networks of three U.S. states. Agent parameters, contact network data, and epidemiological spread patterns are output after each model run. Key network metrics are then calculated, some of which pertain to overall network structure, and others to each node's positionality within the network. We run statistical tests to evaluate the extent to which each network metric predicts epidemiological vulnerability, finding significant correlations in some cases, but no individual metric that serves as a reliable risk indicator. To investigate the complex interactions between network structure and node positionality, we use a genetic programming (GP) algorithm to search for mathematical equations describing combinations of individual metrics — which we call "meta-metrics" — that may better predict vulnerability. We find that the GP solutions — the best of which combine both global and node-level metrics — are far better indicators of disease risk than any individual metric, with meta-metrics explaining up to 91% of the variability in agent vulnerability across all three study areas. We suggest that this methodology could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions, and also that the meta-metric approach may be useful to study a wide range of complex network phenomena.
Xinglong Qu, Zhigang Cao, Xiaoguang Yang and The Anh Han
Journal of Artificial Societies and Social Simulation 22 (3) 5
Kyeywords: Group Cohesion, Public Goods Game, Cooperation Emergence, Conditional Dissociation, Positive Assortment
Abstract: Leaving is usually an option for individuals if they cannot tolerate their defective partners. In a two-player game, when a player chooses to leave, both she and her opponent become single players. However, in a multi-player game, the same decision may have different consequences depending on whether group cohesion exists. Players who choose not to leave would still be united together rather than be separated into singletons if there is cohesion among them. Considering this difference, we study two leaving mechanisms in public goods games. In the first mechanism, every player would be single once any of the group members leaves. In the second, we assume group cohesion exists that members who don't leave form a union. In our model, each player adopts a trigger strategy characterized by a threshold: she leaves if the number of defectors in her group exceeds the threshold. We find that under both mechanisms, when the expected lifespan of individuals is long enough, cooperators with zero tolerance toward defection succeed in the evolution. Moreover, when cohesion exists in groups, cooperation is better promoted because the cooperators have a higher chance to play together. That is, group cohesion facilitates positive assortment and therefore promotes cooperation.
Journal of Artificial Societies and Social Simulation 23 (1) 9
Kyeywords: Agent-Based Modelling, Prisoner’s Dilemma, Pavlovian Cooperation, Collaborative Innovation, Firm Size Distribution, ICT Industry
Abstract: ICT-based Collaborative innovation has a significant impact on the economy by facilitating technological convergence and promoting innovation in other industries. However, research on innovation suggests that polarization in firm size distribution, which has grown since the early 2000s, can interfere with collaborative innovation among firms. In this paper, I modelled firms’ decision-making processes that led to collaborative innovation as a spatial N-person iterated Prisoner’s dilemma (NIPD) game using collaborative innovation data from Korean ICT firms. Using an agent-based model, I experimented with the effects of firm size heterogeneity on collaborative innovation. The simulation experiment results reveal that collaborative innovation in the industry increases as the size heterogeneity decreases. Findings suggest that policies promoting collaborative innovation should focus on mitigating structural inequalities in the industry.
Journal of Artificial Societies and Social Simulation 23 (4) 2
Kyeywords: Altruism, Evolution, Network, Simulation, Small-World
Abstract: The question of why acts of selflessness occur in a Hobbesian self-help world has fascinated scholars for decades, if not centuries. Utilizing simulations, previous research has shown that altruism can be evolutionarily stable in small-scale societies under a narrow set of circumstances. However, when expanding such models to populations of anything larger than a few hundred people, they generally break down. In this paper, I modify the widely used image-score mechanism to include contagion-based reputation and demonstrate how altruism can survive in populations of up to 20,000. I also find that selflessness strongly depends on network topology - as heavily clustered small-world societies that resemble tight-knit family or friendship structures promote more cooperation than random networks where connections are more superficial.
Nicholas LaBerge, Aria Chaderjian, Victor Ginelli, Margrethe Jebsen and Adam Landsberg
Journal of Artificial Societies and Social Simulation 23 (4) 3
Kyeywords: Cultural Evolution, Cultural Transmission, Opinion Dynamics, Agent-Based Modeling, Cultural Dissemination
Abstract: The process by which beliefs, opinions, and other individual, socially malleable attributes spread across a society, known as "cultural dissemination," is a broadly recognized concept among sociologists and political scientists. Yet fundamental aspects of how this process can ultimately lead to cultural divergences between rural and urban segments of society are currently poorly understood. This article uses an agent-based model to isolate and analyze one very basic yet essential facet of this issue, namely, the question of how the intrinsic differences in urban and rural population densities influence the levels of cultural homogeneity/heterogeneity that emerge within each region. Because urban and rural cultures do not develop in isolation from one another, the dynamical interplay between the two is of particular import in their evolution. It is found that, in urban areas, the relatively high number of local neighbors with whom one can interact tends to promote cultural homogeneity in both urban and rural regions. Moreover, and rather surprisingly, the higher frequency of potential interactions with neighbors within urban regions promotes homogeneity in urban regions but tends to drive rural regions towards greater levels of heterogeneity.
Ngan Nguyen, Hongfei Chen, Benjamin Jin, Walker Quinn, Conrad Tyler and Adam Landsberg
Journal of Artificial Societies and Social Simulation 24 (4) 5
Kyeywords: Cultural Dissemination, Agent-Based Modeling, Cultural Evolution, Opinion Dynamics, Cultural Transmission, Bounded Confidence Models
Abstract: We study cultural dissemination in the context of an Axelrod-like agent-based model describing the spread of cultural traits across a society, with an added element of social influence. This modification produces absorbing states exhibiting greater variation in number and size of distinct cultural regions compared to the original Axelrod model, and we identify the mechanism responsible for this amplification in heterogeneity. We develop several new metrics to quantitatively characterize the heterogeneity and geometric qualities of these absorbing states. Additionally, we examine the dynamical approach to absorbing states in both our Social Influence Model as well as the Axelrod Model, which not only yields interesting insights into the differences in behavior of the two models over time, but also provides a more comprehensive view into the behavior of Axelrod's original model. The quantitative metrics introduced in this paper have broad potential applicability across a large variety of agent-based cultural dissemination models.
Molood Ale Ebrahim Dehkordi, Amineh Ghorbani, Giangiacomo Bravo, Mike Farjam, René van Weeren, Anders Forsman and Tine De Moor
Journal of Artificial Societies and Social Simulation 24 (4) 7
Kyeywords: Institutional Modelling, Historical Data, CPRs, Institutional Evolution
Abstract: Historical data are valuable resources for providing insights into social patterns in the past. However, these data often inform us at the macro-level of analysis but not about the role of individuals’ behaviours in the emergence of long-term patterns. Therefore, it is difficult to infer ‘how’ and ‘why’ certain patterns emerged in the past. Historians use various methods to draw hypotheses about the underlying reasons for emerging patterns and trends, but since the patterns are the results of hundreds if not thousands of years of human behaviour, these hypotheses can never be tested in reality. Our proposition is that simulation models and specifically, agent-based models (ABMs) can be used as complementary tools in historical studies to support hypothesis building. The approach that we propose and test in this paper is to design and configure models in such a way as to generate historical patterns, consequently aiming to find individual-level explanations for the emerging pattern. In this work, we use an existing, empirically validated, agent-based model of common pool resource management to test hypotheses formulated based on a historical dataset. We first investigate whether the model can replicate various patterns observed in the dataset, and second, whether it can contribute to a better understanding of the underlying mechanism that led to the observed empirical trends. We showcase how ABM can be used as a complementary tool to support theory development in historical studies. Finally, we provide some guidelines for using ABM as a tool to test historical hypotheses.
Carlos A. de Matos Fernandes, Andreas Flache, Dieko M. Bakker and Jacob Dijkstra
Journal of Artificial Societies and Social Simulation 25 (1) 6
Kyeywords: Cooperation, Meritocratic Matching, Information, Homophily, Threshold Model, Learning
Abstract: Meritocratic matching solves the problem of cooperation by ensuring that only prosocial agents group together while excluding proselfs who are less inclined to cooperate. However, matching is less effective when estimations of individual merit rely on group-level outcomes. Prosocials in uncooperative groups are unable to change the nature of the group and are themselves forced to defect to avoid exploitation. They are then indistinguishable from proselfs, preventing them from accessing cooperative groups. We investigate informal social networks as a potential solution. Interactions in dyadic network relations provide signals of individual cooperativeness which are easier to interpret. Network relations can thus help prosocials to escape from uncooperative groups. To test our intuitions, we develop an ABM modeling cooperative behavior based on a stochastic learning model with adaptive thresholds. We investigate both randomly and homophilously formed networks. We find that homophilous networks create conditions under which meritocratic matching can function as intended. Simulation experiments identify two underlying reasons. First, dyadic network interactions in homophilous networks differentiate more between prosocials and proselfs. Second, homophilous networks create groups of prosocial agents who are aware of each other’s behavior. The stronger this prosociality segregation is, the more easily prosocials cooperate in the group context. Further analyses also highlight a downside of homophilous networks. When prosocials successfully escape from uncooperative groups, non-cooperatives have fewer encounters with prosocials, diminishing their chances to learn to cooperate through those encounters.
Juan Francisco Robles, Enrique Bermejo, Manuel Chica and Óscar Cordón
Journal of Artificial Societies and Social Simulation ()
Kyeywords: Agent-Based Modelling, Model Validation, Automatic Calibration, Multimodal Optimisation, Multimodal Evolutionary Algorithms
Abstract: Agent-based modelling usually involves a calibration stage where a set of parameters needs to be estimated. The calibration process can be automatically performed by using calibration algorithms which search for an optimal parameter configuration to obtain quality model fittings. This issue makes the use of multimodal optimisation methods interesting for calibration as they can provide diverse solution sets with similar and optimal fitness. In this contribution, we compare nine competitive multimodal evolutionary algorithms, both classical and recent, to calibrate agent-based models. We analyse the performance of each multimodal evolutionary algorithm on 12 problem instances of an agent-based model for marketing (i.e. 12 different virtual markets) where we calibrate 24 to 129 parameters to generate two main outputs: historical brand awareness and word-of-mouth volume. Our study shows a clear dominance of SHADE, L-SHADE, and NichePSO over the rest of the multimodal evolutionary algorithms. We also highlight the benefits of these methods for helping modellers to choose from among the best calibrated solutions.