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35 articles matched your search for the keywords:
Agent Based Social Simulation, Trust, Reputation, Cognitive Modeling, Multi-Modal Logic

Normative Reputation and the Costs of Compliance

Cristiano Castelfranchi, Rosaria Conte and Mario Paolucci
Journal of Artificial Societies and Social Simulation 1 (3) 3

Kyeywords: Norms, Reputation, Compliance
Abstract: In this paper, the role of normative reputation in reducing the costs of complying with norms will be explored. In previous simulations (Conte & Castelfranchi 1995), in contrast to a traditional view of norms as means for increasing co-ordination among agents, the effects of normative and non-normative strategies in the control of aggression among agents in a common environment was confronted. Normative strategies were found to reduce aggression to a much greater extent than non-normative strategies, and also to afford the highest average strength and the lowest polarisation of strength among the agents. The present study explores the effects of the interaction between populations following different criteria for aggression control. In such a situation the normative agents alone bear the cost of norms, due to their less aggressive behaviour, while other agents benefit from their presence. Equity is then restored by raising the cost of aggression through the introduction of agents' reputation. This allows normative agents to avoid respecting the cheaters' private property, and to impose a price for transgression. The relevance of knowledge communication is then emphasised by allowing neighbour normative agents to communicate. In particular, the spreading of agents' reputation via communication allows normative agents to co-operate without deliberation at the expense of non-normative agents, thereby redistributing the costs of normative strategies.

Intelligent Social Learning

Rosaria Conte and Mario Paolucci
Journal of Artificial Societies and Social Simulation 4 (1) 3

Kyeywords: Social Learning, Social Facilitation, Cognitive Modeling
Abstract: One of the cognitive processes responsible for social propagation is social learning, broadly meant as the process by means of which agents' acquisition of new information is caused or favoured by their being exposed to one another in a common environment. Social learning results from one or other of a number of social phenomena, the most important of which are social facilitation and imitation. In this paper, a general notion of social learning will be defined and the main processes that are responsible for it, namely social facilitation and imitation, will be analysed in terms of the social mental processes they require. A brief analysis of classical definitions of social learning is carried on, showing that a systematic and consistent treatment of this notion is still missing. A general notion of social learning is then introduced and the two main processes that may lead to it, social facilitation and imitation, will be defined as different steps on a continuum of cognitive complexity. Finally, the utility of the present approach is discussed. The analysis presented in this paper draws upon a cognitive model of social action (cf. Conte & Castelfranchi 1995; Conte 1999). The agent model that will be referred to throughout the paper is a cognitive model, endowed with mental properties for pursuing goals and intentions, and for knowledge-based action. To be noted, a cognitive agent is not to be necessarily meant as a natural system, although many examples examined in the paper are drawn from the real social life of humans. Cognitive agents may also be artificial systems endowed with the capacity for reasoning, planning, and decision-making about both world and mental states. Finally, some advantages of intelligent social learning in agent systems applications are discussed. Keywords:

The Creation of a Reputation in an Artificial Society Organised by a Gift System

Juliette Rouchier, Martin O'Connor and François Bousquet
Journal of Artificial Societies and Social Simulation 4 (2) 8

Kyeywords: Gift, Reputation, Multi-Agent Systems
Abstract: This paper describes simulations in an artificial society in which autonomous agents exchange gifts. In this society agents perform simple acts that are looked at by the others and are analysed so that a common image is created for each agent (a reputation). The model is based on numerous descriptions of non-merchant exchange systems, which are very interesting for ethnologists as well as for economists: they appear to be important for circulation of goods and to insure the reproduction of social links and values. In the system built the agents must make a gift at each time-step. There exist two kinds of gifts and two corresponding kinds of reputation: the agents either give to share or to be prestigious. Since gifts are received according to status, receiving a gift is as important for a reputation as making one. Each agent is characterised by its ''motivation'' to acquire the reputation of being a sharing agent or a prestigious agent. It is also characterised by its ''esteem'', to decide if it will be able to do the gift it wants to do for a time-step. These two characteristics of an agent can be stable during the simulation, but can also evolve according to its history. We study here the different patterns that can appear in the societies, in terms of generation of reputation, and of histories over time. A huge range of these patterns can be observed, depending on the choice made for the parameters. In some cases the agents cannot be individually distinguished, in other cases they can: but, in any case any individual behaviours that emerge have to be sustained by a collective specification that points out more or less the way agents value each reputation.

Group Reputation Supports Beneficent Norms

David Hales
Journal of Artificial Societies and Social Simulation 5 (4) 4

Kyeywords: Norms, Reputation, Social Groups, Group Reputation, Stereotypes
Abstract: This paper demonstrates the role of group normative reputation in the promotion of an aggression reducing possession norm in an artificial society. A previous model of normative reputation is extended such that agents are given the cognitive capacity to categorise other agents as members of a group. In the previous model reputational information was communicated between agents concerning individuals. In the model presented here reputations are projected onto whole groups of agents (a form of "stereotyping"). By stereotyping, norm followers outperform cheaters (who do not follow the norm) under certain conditions. Stereotyping, by increasing the domain of applicability of a piece of reputational information, allows agents to make informed decisions concerning interactions with agents which no other agent has previously met. However, if conditions are not conducive, stereotyping can completely negate norm following behaviour. Group reputation can be a powerful mechanism, therefore, for the promotion of beneficent norms under the right conditions.

Simulating Organizational Decision-Making Using a Cognitively Realistic Agent Model

Ron Sun and Isaac Naveh
Journal of Artificial Societies and Social Simulation 7 (3) 5

Kyeywords: Cognition, Cognitive Architecture, Cognitive Modeling, Classification Decision Making
Abstract: Most of the work in agent-based social simulation has assumed highly simplified agent models, with little attention being paid to the details of individual cognition. Here, in an effort to counteract that trend, we substitute a realistic cognitive agent model (CLARION) for the simpler models previously used in an organizational design task. On that basis, an exploration is made of the interaction between the cognitive parameters that govern individual agents, the placement of agents in different organizational structures, and the performance of the organization. It is suggested that the two disciplines, cognitive modeling and social simulation, which have so far been pursued in relative isolation from each other, can be profitably integrated.

Responsibility for Societies of Agents

Rosaria Conte and Mario Paolucci
Journal of Artificial Societies and Social Simulation 7 (4) 3

Kyeywords: Responsibility, Agents, Cognitive Modeling, E-Governance, Organisation Theory
Abstract: This paper presents a pre-formal social cognitive model of social responsibility as implying the deliberative capacity of the bearer but not necessarily her decision to act or not. Also, responsibility is defined as an objective property of agents, which they cannot remit at their will. Two specific aspects are analysed: (a) the action of "counting upon" given agents as responsible entities, and (b) the consequent property of accountability: responsibility allows to identify the locus of accountability, that is, which agents are accountable for which events and to what extent. Agents responsible for certain events, and upon which others count, are asked to account or respond for these events. Two types of responsibility are distinguished and their commonalities pointed out: (a) a primary form of responsibility, which is a consequence of mere deliberative power, and (b) a task-based form, which is a consequence of task commitment. Primary responsibility is a relation between deliberative agents and social harms, whether these are intended and believed or not, and whether they are actually caused by the agent or not. The boundaries of responsibility will be investigated, and the conceptual links of responsibility with obligation and guilt will be examined. Task-based responsibility implies task- or role-commitment. Furthermore, individual Vs. shared Vs. collective responsibility are distinguished. Considerations about the potential benefits and utility of the analysis proposed for in the field of e-governance are highlighted. Concluding remarks and ideas for future works are discussed in the final section.

Reciprocity, Sanctions, and the Development of Mutual Obligation in Egalitarian Societies

Stephen Younger
Journal of Artificial Societies and Social Simulation 8 (2) 9

Kyeywords: Reciprocity, Normative Reputation, Mutual Obligation, Gift-Giving Societies
Abstract: Discrete agent simulation was used to study several models of reciprocity and sanctions in a model egalitarian society. We found that mutual obligation between agents was maximized for indiscriminant sharing, the same condition that has been observed in several traditional cultures. Alternate sharing strategies, including ones based on kinship or sharing with those who share in return, reduced mutual obligation. When theft and sanctions were introduced into the simulations, we found that mutual obligation was maximized when individual norms were strong, i.e. when there was little tolerance to theft. Collective sanctions, represented by the ostracism of non-normative agents, produced levels of mutual obligation comparable to the case of strong individual norms, but with significant risk of population collapse. The probability of long term survival was highest when tolerance to transgressions was either very low or very high and we propose that this may be one reason for the similarity of normative systems across diverse egalitarian cultures.

Towards Good Social Science

Scott Moss and Bruce Edmonds
Journal of Artificial Societies and Social Simulation 8 (4) 13

Kyeywords: Methodology, Agent Based Social Simulation, Qualitative Analysis; Evidence; Conditions of Application; History of Science
Abstract: The paper investigates what is meant by "good science" and "bad science" and how these differ as between the natural (physical and biological) sciences on the one hand and social sciences on the other. We conclude on the basis of historical evidence that the natural science are much more heavily constrained by evidence and observation than by theory while the social sciences are constrained by prior theory and hardly at all by direct evidence. Current examples of the latter proposition are taken from recent issues of leading social science journals. We argue that agent based social simulations can be used as a tool to constrain the development of a new social science by direct (what economists dismiss as anecdotal) evidence and that to do so would make social science relevant to the understanding and influencing of social processes. We argue that such a development is both possible and desirable. We do not argue that it is likely.

Introduction to the Special Section on Reputation in Agent Societies

Mario Paolucci and Jordi Sabater-Mir
Journal of Artificial Societies and Social Simulation 9 (1) 16

Kyeywords: Reputation, Agent Systems
Abstract: This special section includes papers from the 'Reputation in Agent Societies' workshop held as part of 2004 IEEE/WIC/ACM International Joint Conference on Intelligent Agent Technology (IAT'04) and Web Intelligence (WI'04), September 20, 2004 in Beijing, China. The purpose of this workshop was to promote multidisciplinary collaboration for Reputation Systems modeling and implementation. Reputation is increasingly at the centre of attention in many fields of science and domains of application, including economics, organisations science, policy-making, (e-)governance, cultural evolution, social dilemmas, socio-dynamics, innofusion, etc. However, the result of all this attention is a great number of ad hoc models and little integration of instruments for the implementation, management and optimisation of reputation. On the one hand, entrepreneurs and administrators manage corporate and firm reputation without contributing to or accessing a solid, general and integrated body of scientific knowledge on the subject matter. On the other hand, software designers believe they can design and implement online reputation reporting systems without investigating what the properties, requirements and dynamics of reputation in natural societies are and why it evolved. We promoted the workshop and this special section with the hope of setting the first steps in the direction of a new, cross-disciplinary approach to reputation, accounting for the social cognitive mechanisms and processes that support it and working towards t a consensus on essential guidelines for designing or shaping reputation technologies.

Evolution of Cooperation when Feedback to Reputation Scores is Voluntary

Marco A. Janssen
Journal of Artificial Societies and Social Simulation 9 (1) 17

Kyeywords: Trust, Reputation, One-Shot Prisoner Dilemma, Voluntary Feedback, Symbols
Abstract: Reputation systems are used to facilitate interaction between strangers in one-shot social dilemmas, like transactions in e-commerce. The functioning of various reputation systems depend on voluntary feedback derived from the participants in those social dilemmas. In this paper a model is presented under which frequencies of providing feedback to positive and negative experiences in reputation systems explain observed levels of cooperation. The results from simulations show that it is not likely that reputation scores alone will lead to high levels of cooperation.

On the Simulation of Global Reputation Systems

Andreas Schlosser, Marco Voss and Lars Brückner
Journal of Artificial Societies and Social Simulation 9 (1) 4

Kyeywords: Reputation System, Trust, Formalization, Simulation
Abstract: Reputation systems evolve as a mechanism to build trust in virtual communities. In this paper we evaluate different metrics for computing reputation in multi-agent systems. We present a formal model for describing metrics in reputation systems and show how different well-known global reputation metrics are expressed by it. Based on the model a generic simulation framework for reputation metrics was implemented. We used our simulation framework to compare different global reputation systems to find their strengths and weaknesses. The strength of a metric is measured by its resistance against different threat-models, i.e. different types of hostile agents. Based on our results we propose a new metric for reputation systems.

Repage: REPutation and ImAGE Among Limited Autonomous Partners

Jordi Sabater-Mir, Mario Paolucci and Rosaria Conte
Journal of Artificial Societies and Social Simulation 9 (2) 3

Kyeywords: Reputation, Agent Systems, Cognitive Design, Fuzzy Evaluation
Abstract: This paper introduces Repage, a computational system that adopts a cognitive theory of reputation. We propose a fundamental difference between image and reputation, which suggests a way out from the paradox of sociality, i.e. the trade-off between agents' autonomy and their need to adapt to social environment. On one hand, agents are autonomous if they select partners based on their social evaluations (images). On the other, they need to update evaluations by taking into account others'. Hence, social evaluations must circulate and be represented as "reported evaluations" (reputation), before and in order for agents to decide whether to accept them or not. To represent this level of cognitive detail in artificial agents' design, there is a need for a specialised subsystem, which we are in the course of developing for the public domain. In the paper, after a short presentation of the cognitive theory of reputation and its motivations, we describe the implementation of Repage.

Social Reputation: a Mechanism for Flexible Self-Regulation of Multiagent Systems

Christian Hahn, Bettina Fley, Michael Florian, Daniela Spresny and Klaus Fischer
Journal of Artificial Societies and Social Simulation 10 (1) 2

Kyeywords: Reputation; Institution; Electronic Market; Self-Regulation; Multiagent System
Abstract: In this paper, we use multiagent technology for social simulation of sociological micro-macro issues in the domain of electronic marketplaces. We argue that allowing self-interested agents to enable social reputation as a mechanism for flexible self-regulation during runtime can improve the robustness and \'social order\' of multiagent systems to cope with various perturbations that arise when simulating open markets (e.g. dynamic modifications of task profiles, scaling of agent populations, agent drop-outs, deviant behaviour). Referring to the sociological theory of Pierre Bourdieu, we provide a multi-level concept of reputation that consists of three different types (image, social esteem, and prestige) and considers reputation as a kind of \'symbolic capital\'. Reputation is regarded to be objectified as an observable property and to be incorporated into the agents\' mental structures through social practices of communication on different aggregation levels of sociality. We present and analyse selected results of our social simulations and discuss the importance of reputation with regard to the robustness of multiagent simulations of electronic markets.

Communication Between Process and Structure: Modelling and Simulating Message Reference Networks with COM/TE

Thomas Malsch, Christoph Schlieder, Peter Kiefer, Maren Lübcke, Rasco Perschke, Marco Schmitt and Klaus Stein
Journal of Artificial Societies and Social Simulation 10 (1) 9

Kyeywords: Communication, Communication-Oriented Modelling, Message Sign, Dynamic Networks, Bottom-up Approach, Temporality, Social Visibility, Reputation, Socionics
Abstract: Focusing on observable message signs and referencing structures, communication processes can be described and analysed as message reference networks which are characterized by dynamic pattern evolution. Computational simulation provides a way of obtaining insights into the factors driving such processes. Our paper describes a theoretical framework for communication-oriented modelling — the COM approach — that is centred around the notion of social visibility as a reputation mechanism. The approach contrasts with agent-based social networks on the one hand, and with bibliometric document networks on the other. In introducing our simulation environment COM/TE, typical properties of message reference networks are discussed in terms of a case study which deals with the impact of different media and styles of communication on emergent patterns of social visibility.

Agent-Based Simulation of the Trust and Tracing Game for Supply Chains and Networks

Dmytro Tykhonov, Catholijn Jonker, Sebastiaan Meijer and Tim Verwaart
Journal of Artificial Societies and Social Simulation 11 (3) 1

Kyeywords: Trust, Deception, Supply Chain, Multi-Agent System, Simulation
Abstract: This paper describes a multi-agent simulation model of the Trust And Tracing game. The Trust And Tracing game is a gaming simulation for human players, developed as a research tool for data collection on human behaviour in food supply chains with asymmetric information about food quality and food safety. Important issues in the game are opportunistic behaviour (deceit), trust and institutional arrangements for enforcing compliance. The goal is to improve the understanding of human decision making with respect to these issues. To this end multi-agent simulation can be applied to simulate the effect of models of individual decision making in partner selection, negotiation, deceit and trust on system behaviour. The combination of human gaming simulation and multi-agent simulation offers a basis for model refinement in a cycle of validation, experimentation, and formulation of new hypotheses. This paper describes a first round of model formulation and validation. The models presented are validated by a series of experiments performed by the implemented simulation system, of which the outcomes are compared on aggregated level to the outcomes of games played by humans. The experiments cover in a systematic way the important variations in parameter settings possible in the game and in the characteristics of the agents. The simulation results show the same tendencies of behaviour as the observed human games.

A Replication That Failed - on the Computational Model in 'Michael W. Macy and Yoshimichi Sato: Trust, Cooperation and Market Formation in the U.S. and Japan. Proceedings of the National Academy of Sciences, May 2002'

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.

Reply to Will and Hegselmann

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]

Modelling Opinion Formation with Physics Tools: Call for Closer Link with Reality

Pawel Sobkowicz
Journal of Artificial Societies and Social Simulation 12 (1) 11

Kyeywords: Methodology, Agent Based Social Simulation, Qualitative Analysis; Evidence; Conditions of Application
Abstract: The growing field of studies of opinion formation using physical formalisms and computer simulation based tools suffers from relative lack of connection to the 'real world' societal behaviour. Such sociophysics research should aim at explaining observations or at proposing new ones. Unfortunately, this is not always the case, as many works concentrate more on the models themselves than on the social phenomena. Moreover, the simplifications proposed in simulations often sacrifice realism on the altar of computability. There are several ways to improve the value of the research, the most important by promoting truly multidisciplinary cooperation between physicists aiming to describe social phenomena and sociologists studying the phenomena in the field. In the specific case of modelling of opinion formation there are a few technical ideas which might bring the computer models much closer to reality, and therefore to improve the predictive value of the sociophysics approach.

Effects of a Trust Mechanism on Complex Adaptive Supply Networks: An Agent-Based Social Simulation Study

Whan-Seon Kim
Journal of Artificial Societies and Social Simulation 12 (3) 4

Kyeywords: Complex Adaptive System, Agent-Based Social Simulation, Supply Network, Trust
Abstract: This paper models a supply network as a complex adaptive system (CAS), in which firms or agents interact with one another and adapt themselves. And it applies agent-based social simulation (ABSS), a research method of simulating social systems under the CAS paradigm, to observe emergent outcomes. The main purposes of this paper are to consider a social factor, trust, in modeling the agents\' behavioral decision-makings and, through the simulation studies, to examine the intermediate self-organizing processes and the resulting macro-level system behaviors. The simulations results reveal symmetrical trust levels between two trading agents, based on which the degree of trust relationship in each pair of trading agents as well as the resulting collaboration patterns in the entire supply network emerge. Also, it is shown that agents\' decision-making behavior based on the trust relationship can contribute to the reduction in the variability of inventory levels. This result can be explained by the fact that mutual trust relationship based on the past experiences of trading diminishes an agent\'s uncertainties about the trustworthiness of its trading partners and thereby tends to stabilize its inventory levels.

Resolving a Replication That Failed: News on the Macy & Sato Model

Oliver Will
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.

Social Simulation and Analysis of the Dynamics of Criminal Hot Spots

Tibor Bosse and Charlotte Gerritsen
Journal of Artificial Societies and Social Simulation 13 (2) 5

Kyeywords: Agent-Based Modelling, Criminal Hot Spots, Displacement, Reputation, Social Simulation, Analysis
Abstract: Within the field of Criminology, the spatio-temporal dynamics of crime are an important subject of study. In this area, typical questions are how the behaviour of offenders, targets, and guardians can be explained and predicted, as well as the emergence and displacement of criminal hot spots. In this article we present a combination of software tools that can be used as an experimental environment to address such questions. In particular, these tools comprise an agent-based simulation model, a verification tool, and a visualisation tool. The agent-based simulation model specifically focuses on the interplay between hot spots and reputation. Using this environment, a large number of simulation runs have been performed, of which results have been formally analysed. Based on these results, we argue that the presented environment offers a valuable approach to analyse the dynamics of criminal hot spots.

The Surprising Success of a Replication That Failed

Michael Macy and Yoshimichi Sato
Journal of Artificial Societies and Social Simulation 13 (2) 9

Kyeywords: Trust, Mobility, Replication
Abstract: In a recent paper (jasss.soc.surrey.ac.uk/12/4/11.html), Oliver Will contends that the effect of mobility on trust that we originally reported (2002) depends on \'an assumption that is most probably an unwilling, unintended, and unwanted implication of the code.\' When we experimented with Will\'s revised model, we came to the opposite conclusion: his version provides stronger support for our theory than does our original. The explanation is that Will left the learning rate at the upper limit of 1.0, the level we assumed in our original paper. When we lowered the learning rate to compensate for the removal of the contested assumption, the results showed how mobility can lead to an increase in trust, which is consistent with our explanation for higher trust in the US compared to Japan. Moreover, the model also shows that it is possible for there to be too much mobility.

Why Bother with What Others Tell You? An Experimental Data-Driven Agent-Based Model

Riccardo Boero, Giangiacomo Bravo, Marco Castellani and Flaminio Squazzoni
Journal of Artificial Societies and Social Simulation 13 (3) 6

Kyeywords: Reputation, Trustworthiness, Laboratory Experiment, Agent-Based Model, Exploration Vs. Exploitation
Abstract: This paper investigates the relevance of reputation to improve the explorative capabilities of agents in uncertain environments. We have presented a laboratory experiment where sixty-four subjects were asked to take iterated economic investment decisions. An agent-based model based on their behavioural patterns replicated the experiment exactly. Exploring this experimentally grounded model, we studied the effects of various reputational mechanisms on explorative capabilities at a systemic level. The results showed that reputation mechanisms increase the agents\' capability for coping with uncertain environments more than individualistic atomistic exploration strategies, although the former does entail a certain amount of false information inside the system.

Fairness Emergence in Reputation Systems

Adam Wierzbicki and Radoslaw Nielek
Journal of Artificial Societies and Social Simulation 14 (1) 3

Kyeywords: Trust, Simulation, Fairness, Equity, Emergence, Reputation System
Abstract: Reputation systems have been used to support users in making decisions under uncertainty or risk that is due to the autonomous behavior of others. Research results support the conclusion that reputation systems can protect against exploitation by unfair users, and that they have an impact on the prices and income of users. This observation leads to another question: can reputation systems be used to assure or increase the fairness of resource distribution? This question has a high relevance in social situations where, due to the absence of established authorities or institutions, agents need to rely on mutual trust relations in order to increase fairness of distribution. This question can be formulated as a hypothesis: in reputation (or trust management) systems, fairness should be an emergent property. The notion of fairness can be precisely defined and investigated based on the theory of equity. In this paper, we investigate the Fairness Emergence hypothesis in reputation systems and prove that , under certain conditions, the hypothesis is valid for open and closed systems, even in unstable system states and in the presence of adversaries. Moreover, we investigate the sensitivity of Fairness Emergence and show that an improvement of the reputation system strengthens the emergence of fairness. Our results are confirmed using a trace-driven simulation from a large Internet auction site.

A Computational Model of Worker Protest

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.

Computational Modelling of Trust and Social Relationships

Alistair Sutcliffe and Di Wang
Journal of Artificial Societies and Social Simulation 15 (1) 3

Kyeywords: Social Agents, Social Modelling, Trust, Social Networks
Abstract: A computational model for the development of social relationships is described. The model implements agent strategies for social interaction based on Dunbar's Social Brain Hypothesis (SBH). 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 the SBH predictions was found across a range of model parameter settings, which varied the waning rate of trust, defect/cooperation rates for agents, and linear/log functions for trust increase and decay. Social interaction strategies which favour interacting with existing strong ties or a time variant strategy produced more SBH conformant results than strategies favour more weaker relationships. The prospects for modeling the emergence of social relationships are discussed.

Flora: A Testbed for Evaluating the Potential Impact of Proposed Systems on Population Wellbeing

Edgar Sioson
Journal of Artificial Societies and Social Simulation 15 (3) 6

Kyeywords: Simulation Testbed, Reputation Systems, Decentralized Currency, Modular Framework, Agent-Based Model
Abstract: We present Flora, a testbed that supports multidimensional fitness and resource modeling. Its main features are evaluation metrics related to population wellbeing, scalable representation of resource diversity, and composability of sociotechnical test scenarios through the TDI framework. We ran simulations to illustrate Flora's use in modeling the effects of using information infrastructures with different component systems. We analyzed the impact of hoarders in the absence of accounting systems, compared the performance of different decentralized currency systems in terms of accounting design features, and modeled the potential impact of reputation systems in deterring detrimental socioeconomic behavior. Among findings were the importance of having resource diversity as well as resources that each could target different fitness dimension needs; the inherent robustness of accounting systems that allow organizations to set budgets independently of centrally issued currency; and the greater effectiveness of buyer-screening compared to seller-screening as a means for influencing malevolent socioeconomic actors.

Logic-Based Reputation Model in E-Commerce Simulation

Ioan Alfred Letia and Radu Razvan Slavescu
Journal of Artificial Societies and Social Simulation 15 (3) 7

Kyeywords: Agent Based Social Simulation, Trust, Reputation, Cognitive Modeling, Multi-Modal Logic
Abstract: We employ a multimodal logic in a decision making mechanism involving trust and reputation. The mechanism is then used in a community of interacting agents which develop cooperative relationships, assess the results against several quality criteria and possibly publish their beliefs inside the group. A new definition is proposed for describing how an agent deals with the common reputation information and with divergent opinions. The definition permits selecting and integrating the knowledge obtained from the peers, based on their perceived trust, as well as on threshold called critical mass. The influence of this parameter and of the number of agents supporting a sentence over its adoption are then investigated.

Social Relationships and the Emergence of Social Networks

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.

Studying Web Content Credibility by Social Simulation

Adam Wierzbicki, Paulina Adamska, Katarzyna Abramczuk, Thanasis Papaioannou, Karl Aberer and Emilia Rejmund
Journal of Artificial Societies and Social Simulation 17 (3) 6

Kyeywords: Credibility, Reputation, Game Theory, Incentives, Online Communities
Abstract: The Internet has become an important source of information that significantly affects social, economical and political life. The content available in the Web is the basis for the operation of the digital economy. Moreover, Web content has become essential for many Web users that have to make decisions. Meanwhile, more and more often we encounter Web content of low credibility due to incorrect opinions, lack of knowledge, and, even worse, manipulation attempts for the benefit of the authors or content providers. While mechanisms for the support of credibility evaluation in practice have been proposed, little is known about their effectiveness, and about their influence on the global picture of Web content production and consumption. We use a game-theoretic model to analyze the impact of reputation on the evaluation of content credibility by consumers with varying expertise, in the presence of producers who have incentives to manipulate information.

Network-Based Trust Games: An Agent-Based Model

Shu-Heng Chen, Bin-Tzong Chie and Tong Zhang
Journal of Artificial Societies and Social Simulation 18 (3) 5

Kyeywords: Trust Game, Network Game, Multiplier, Clique, Stochastic Choice, Myopic Trust, Relative Reciprocity
Abstract: By hybridizing two kinds of games frequently used in experimental economics, namely, trust games and network games, this paper develops a model of the network-based trust game. Through agent-based simulation of the model, we can demonstrate the positive effects of trust on growth. Even though the underlying technology still provides the fundamental channel for growth, there is an indirect effect on growth through network formation. It is in this network formation process that trust plays a role. The trust considered in this paper is a kind of myopic trust which, through the stochastic choice model, can affect agents' decisions regarding networking, portfolios, and kickbacks, which in turn affects network formation, wealth creation, and distribution.

From Beliefs to Attitudes: Polias, a Model of Attitude Dynamics Based on Cognitive Modeling and Field Data

Kei-Leo Brousmiche, Jean-Daniel Kant, Nicolas Sabouret and François Prenot-Guinard
Journal of Artificial Societies and Social Simulation 19 (4) 2

Kyeywords: Social Simulation, Attitude Formation, Cognitive Modeling, Calibration Using Field Data
Abstract: Attitude is a key concept in social psychology. The paper presents a novel agent-based model to simulate attitude formation by combining a rational and an emotional components based on cognitive, psychological and social theories. Individuals of the artificial population perceive actions taken by actors such as government or brands, they form an attitude toward them and also communicate the events through a social network. The model outputs are first studied through a functional analysis in which some unique macroscopic behaviors have emerged such as the impact of social groups, the resistance of the population toward disinformation campaigns or the social pressure. We then applied our model on a real world scenario depicting the effort of French Forces in their stabilization operations in Kapisa (Afghanistan) between 2010 and 2012. We calibrated the model parameters based on this scenario and the results of opinion polls that were conducted in the area during the same period about the sentiment of the population toward the Forces. Our model was able to reproduce polls results with a global error under 3%. Based on these results, we show the different dynamics tendencies that emerged among the population by applying a non-supervised classification algorithm.

Role of Trust in a Self-Organizing Pharmaceutical Supply Chain Model with Variable Good Quality and Imperfect Information

Graeme J. Ackland, Edmund Chattoe-Brown, Heather Hamill, Kate R. Hampshire, Simon Mariwah and Gerry Mshana
Journal of Artificial Societies and Social Simulation 22 (2) 5

Kyeywords: Trust, Africa, Supply Chain, Self-Organization, Drugs, Medicine
Abstract: We present an Agent-Based Model (hereafter ABM) for a pharmaceutical supply chain operating under conditions of weak regulation and imperfect information, exploring the possibility of poor quality medicines and their detection. Our interest is to demonstrate how buyers can learn about the quality of sellers (and their medicines) based on previous successful and unsuccessful transactions, thereby establishing trust over time. Furthermore, this network of trust allows the system itself to evolve to positive outcomes (under some but not all circumstances) by eliminating sellers with low quality products. The ABM we develop assumes that rational and non-corrupt agents (wholesalers, retailers and consumers) learn from experience and adjust their behaviour accordingly. The system itself evolves over time: under some - but not all - circumstances, sellers with low-quality products are progressively eliminated. Three distinct states of the supply chain are observed depending on the importance of trust built up from past experience. The 'dynamic' state is characterised by a low level of trust leading to a continually changing system with new drugs introduced and rejected with little regard to quality. The 'frozen' state arises from high levels of reliance on past experience and locks the supply chain into a suboptimal state. The 'optimising' state has moderate reliance on past experience and leads to the persistence of suppliers with good quality; however, the system is still 'invadable' by better quality drugs. Simulation results show that the state reached by the system depends strongly on the precise way that trust is established: Excessive levels of trust make it impossible for new, improved treatments to be adopted. This highlights the critical need to understand better how personal experience influences consumer behaviour, especially where regulation is weak and for products like medicines whose quality is not readily observable.

Emergence of Small-World Networks in an Overlapping-Generations Model of Social Dynamics, Trust and Economic Performance

Katarzyna Growiec, Jakub Growiec and Bogumił Kamiński
Journal of Artificial Societies and Social Simulation 23 (2) 8

Kyeywords: Social Network Structure, Social Network Dynamics, Trust, Willingness to Cooperate, Economic Performance, Agent-Based Model
Abstract: We study the impact of endogenous creation and destruction of social ties in an artificial society on aggregate outcomes such as generalized trust, willingness to cooperate, social utility and economic performance. To this end we put forward a computational multi-agent model where agents of overlapping generations interact in a dynamically evolving social network. In the model, four distinct dimensions of individuals’ social capital: degree, centrality, heterophilous and homophilous interactions, determine their generalized trust and willingness to cooperate, altogether helping them achieve certain levels of social utility (i.e., utility from social contacts) and economic performance. We find that the stationary state of the simulated social network exhibits realistic small-world topology. We also observe that societies whose social networks are relatively frequently reconfigured, display relatively higher generalized trust, willingness to cooperate, and economic performance – at the cost of lower social utility. Similar outcomes are found for societies where social tie dissolution is relatively weakly linked to family closeness.

The Role of Reinforcement Learning in the Emergence of Conventions: Simulation Experiments with the Repeated Volunteer’s Dilemma

Hendrik Nunner, Wojtek Przepiorka and Chris Janssen
Journal of Artificial Societies and Social Simulation 25 (1) 7

Kyeywords: Conventions, Repeated Games, Volunteer’s Dilemma, Agent-Based Simulation, Reinforcement Learning, Cognitive Modeling
Abstract: We use reinforcement learning models to investigate the role of cognitive mechanisms in the emergence of conventions in the repeated volunteer’s dilemma (VOD). The VOD is a multi-person, binary choice collective goods game in which the contribution of only one individual is necessary and sufficient to produce a benefit for the entire group. Behavioral experiments show that in the symmetric VOD, where all group members have the same costs of volunteering, a turn-taking convention emerges, whereas in the asymmetric VOD, where one “strong” group member has lower costs of volunteering, a solitary-volunteering convention emerges with the strong member volunteering most of the time. We compare three different classes of reinforcement learning models in their ability to replicate these empirical findings. Our results confirm that reinforcement learning models can provide a parsimonious account of how humans tacitly agree on one course of action when encountering each other repeatedly in the same interaction situation. We find that considering contextual clues (i.e., reward structures) for strategy design (i.e., sequences of actions) and strategy selection (i.e., favoring equal distribution of costs) facilitate coordination when optima are less salient. Furthermore, our models produce better fits with the empirical data when agents act myopically (favoring current over expected future rewards) and the rewards for adhering to conventions are not delayed.