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(25 articles matched your search)

Case-Based Reasoning, Social Dilemmas, and a New Equilibrium Concept

Luis R. Izquierdo, Nicholas M. Gotts and J. Gareth Polhill
Journal of Artificial Societies and Social Simulation 7 (3) 1

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.

The Ghost in the Model (and Other Effects of Floating Point Arithmetic)

J. Gareth Polhill, Luis R. Izquierdo and Nicholas M. Gotts
Journal of Artificial Societies and Social Simulation 8 (1) 5

Abstract: This paper will explore the effects of errors in floating point arithmetic in two published agent-based models: the first a model of land use change (Polhill et al. 2001; Gotts et al. 2003), the second a model of the stock market (LeBaron et al. 1999). The first example demonstrates how branching statements with floating point operands of comparison operators create a high degree of nonlinearity, leading in this case to the creation of 'ghost' agents -- visible to some parts of the program but not to others. A potential solution to this problem is proposed. The second example shows how mathematical descriptions of models in the literature are insufficient to enable exact replication of work since mathematically equivalent implementations in terms of real number arithmetic are not equivalent in terms of floating point arithmetic.

Lessons Learned from Converting the Artificial Stock Market to Interval Arithmetic

J. Gareth Polhill and Luis R. Izquierdo
Journal of Artificial Societies and Social Simulation 8 (2) 2

Abstract: This paper describes work undertaken converting the Artificial Stock Market (LeBaron et al., 1999; Johnson, 2002) to using interval arithmetic instead of floating point arithmetic, the latter having been shown in an earlier article to be the cause of changed behaviour in the ASM (Polhill et al., in press). Results of both a naive (potentially automatable) conversion and one involving a more in-depth analysis of the code are presented that suggest that interval arithmetic may not be the best approach to dealing with the issue of numeric representation in the ASM. We also find that there are good reasons to suspect that floating point errors are not a significant issue for the ASM.

Is Your Model Susceptible to Floating-Point Errors?

Luis R. Izquierdo and J. Gareth Polhill
Journal of Artificial Societies and Social Simulation 9 (4) 4

Abstract: This paper provides a framework that highlights the features of computer models that make them especially vulnerable to floating-point errors, and suggests ways in which the impact of such errors can be mitigated. We focus on small floating-point errors because these are most likely to occur, whilst still potentially having a major influence on the outcome of the model. The significance of small floating-point errors in computer models can often be reduced by applying a range of different techniques to different parts of the code. Which technique is most appropriate depends on the specifics of the particular numerical situation under investigation. We illustrate the framework by applying it to six example agent-based models in the literature.

A Semantic Grid Service for Experimentation with an Agent-Based Model of Land-Use Change

J. Gareth Polhill, Edoardo Pignotti, Nicholas M. Gotts, Pete Edwards and Alun Preece
Journal of Artificial Societies and Social Simulation 10 (2) 2

Abstract: Agent-based models, perhaps more than other models, feature large numbers of parameters and potentially generate vast quantities of results data. This paper shows through the FEARLUS-G project (an ESRC e-Social Science Initiative Pilot Demonstrator Project) how deploying an agent-based model on the Semantic Grid facilitates international collaboration on investigations using such a model, and contributes to establishing rigorous working practices with agent-based models as part of good science in social simulation. The experimental workflow is described explicitly using an ontology, and a Semantic Grid service with a web interface implements the workflow. Users are able to compare their parameter settings and results, and relate their work with the model to wider scientific debate.

Open Access for Social Simulation

J. Gareth Polhill and Bruce Edmonds
Journal of Artificial Societies and Social Simulation 10 (3) 10

Abstract: We consider here issues of open access to social simulations, with a particular focus on software licences, though also briefly discussing documentation and archiving. Without any specific software licence, the default arrangements are stipulated by the Berne Convention (for those countries adopting it), and are unsuitable for software to be used as part of the scientific process (i.e. simulation software used to generate conclusions that are to be considered part of the scientific domain of discourse). Without stipulating any specific software licence, we suggest rights that should be provided by any candidate licence for social simulation software, and provide in an appendix an evaluation of some popularly used licences against these criteria.

Progress in Model-To-Model Analysis

Juliette Rouchier, Claudio Cioffi-Revilla, J. Gareth Polhill and Keiki Takadama
Journal of Artificial Societies and Social Simulation 11 (2) 8

Abstract: [No abstract]

Using the ODD Protocol for Describing Three Agent-Based Social Simulation Models of Land-Use Change

J. Gareth Polhill, Dawn C. Parker, Daniel Brown and Volker Grimm
Journal of Artificial Societies and Social Simulation 11 (2) 3

Abstract: This article describes three agent-based social simulation models in the area of land-use change using a model documentation protocol, ODD, from the ecological literature. Our goal is to evaluate how well fitted it is to social simulations and how successful it might be in increasing communication between individual- and agent-based modellers. Such shared protocols can facilitate model review, comparison, and replication. We initially conclude that the framework holds promise as a standard communication mechanism, although some refinements may be needed.

Agent-Based Urban Land Markets: Agent\'s Pricing Behavior, Land Prices and Urban Land Use Change

Tatiana Filatova, Dawn C. Parker and Anne van der Veen
Journal of Artificial Societies and Social Simulation 12 (1) 3

Abstract: We present a new bilateral agent-based land market model, which moves beyond previous work by explicitly modeling behavioral drivers of land-market transactions on both the buyer and seller side; formation of bid prices (of buyers) and ask prices (of sellers); and the relative division of the gains from trade from the market transactions. We analyze model output using a series of macro-scale economic and landscape pattern measures, including land rent gradients estimated using simple regression models. We first demonstrate that our model replicates relevant theoretical results of the traditional Alonso/Von Thünen model (structural validation). We then explore how urban morphology and land rents change as the relative market power of buyers and sellers changes (i.e., we move from a \'sellers\' market\' to a \'buyers\' market\'). We demonstrate that these strategic price dynamics have differential effects on land rents, but both lead to increased urban expansion.

When and How to Imitate Your Neighbours: Lessons from and for FEARLUS

Nicholas M. Gotts and J. Gareth Polhill
Journal of Artificial Societies and Social Simulation 12 (3) 2

Abstract: This paper summarises some previously published work on imitation, experimentation (or innovation) and aspiration thresholds using the FEARLUS modelling system and reports new work with FEARLUS extending these studies. Results are discussed in the context of existing literature on imitation and innovation in related contexts. A form of imitation in which land uses are selected on the criterion of their recent performance within the neighbourhood of the land parcel concerned (called here 'Best-mean Imitation'), outperforms comparably simple forms of imitation in a wide range of FEARLUS Environments. However, the choice of criterion is shown to interact with both the way the criterion is applied, and the land manager's aspiration threshold: the level of return with which they are satisfied. The implications of work with FEARLUS for the broader bodies of research discussed, and vice versa, are considered.

The Development of Social Simulation as Reflected in the First Ten Years of JASSS: a Citation and Co-Citation Analysis

Matthias Meyer, Iris Lorscheid and Klaus G. Troitzsch
Journal of Artificial Societies and Social Simulation 12 (4) 12

Abstract: Social simulation is often described as a multidisciplinary and fast-moving field. This can make it difficult to obtain an overview of the field both for contributing researchers and for outsiders who are interested in social simulation. The Journal for Artificial Societies and Social Simulation (JASSS) completing its tenth year provides a good opportunity to take stock of what happened over this time period. First, we use citation analysis to identify the most influential publications and to verify characteristics of social simulation such as its multidisciplinary nature. Then, we perform a co-citation analysis to visualize the intellectual structure of social simulation and its development. Overall, the analysis shows social simulation both in its early stage and during its first steps towards becoming a more differentiated discipline.

Using Qualitative Evidence to Enhance an Agent-Based Modelling System for Studying Land Use Change

J. Gareth Polhill, Lee-Ann Sutherland and Nicholas M. Gotts
Journal of Artificial Societies and Social Simulation 13 (2) 10

Abstract: This paper describes and evaluates a process of using qualitative field research data to extend the pre-existing FEARLUS agent-based modelling system through enriching its ontological capabilities, but without a deep level of involvement of the stakeholders in designing the model itself. Use of qualitative research in agent-based models typically involves protracted and expensive interaction with stakeholders; consequently gathering the valuable insights that qualitative methods could provide is not always feasible. At the same time, many researchers advocate building completely new models for each scenario to be studied, violating one of the supposed advantages of the object-oriented programming languages in which many such systems are built: that of code reuse. The process described here uses coded interviews to identify themes suggesting changes to an existing model, the assumptions behind which are then checked with respondents. We find this increases the confidence with which the extended model can be applied to the case study, with a relatively small commitment required on the part of respondents.

ODD Updated

J. Gareth Polhill
Journal of Artificial Societies and Social Simulation 13 (4) 9

Abstract: An update to Volker Grimm and colleagues\' Overview, Design concepts and Details (ODD) protocol for documenting individual and agent based models (I/ABM) has recently been published in Ecological Modelling. This renames the \'State variables and scales\' element to \'Entities, state variables and scales\', and the \'Input\' element to \'Input data\', introduces two new Design concepts (\'Basic principles\' and \'Learning\'), and renames another (\'Fitness\' is now generalised to \'Objectives\'). The Design concepts element can now also be shortened such that it is not required to include any design concept that is irrelevant to the model, and expanded to include new design concepts more appropriate to the model being described. Other clarifications of intentions in the original protocol have been made.

Extracting OWL Ontologies from Agent-Based Models: A Netlogo Extension

J. Gareth Polhill
Journal of Artificial Societies and Social Simulation 18 (2) 15

Abstract: Using OWL ontologies to represent the state and structure of a simulation at any one time has been argued to improve the transparency of a social simulation, on the basis that this information is then not embedded in the source code of the model, or in the computer’s memory at run-time. Should transparency of such a form be desirable, it would be preferable to enable it by extracting the information automatically from a running model. However, semantic differences between traditional object-oriented programming languages and description logics pose an obstacle to this. This paper presents arguments that Netlogo does not have the same semantic challenges to automated ontology extraction, and describes an extension to Netlogo (5.0) using the OWL-API (3.1.0) that extracts state and structure ontologies from an existing Netlogo model. The extension is freely available from https://github.com/garypolhill/netlogo-owl.

"Anarchy" Reigns: A Quantitative Analysis of Agent-Based Modelling Publication Practices in JASSS, 2001-2012

Simon Angus and Behrooz Hassani-Mahmooei
Journal of Artificial Societies and Social Simulation 18 (4) 16

Abstract: Agent Based Modelling (ABM), a promising scientific toolset, has received criticism from some, in part, due to a claimed lack of scientific rigour, especially in the communication of its methods and results. To test the veracity of these claims, we conduct a structured analysis of over 900 scientific objects (figures, tables, or equations) that arose from 128 ABM papers published in the Journal of Artificial Societies and Social Simulation (JASSS), during the period 2001 to 2012 inclusive. Regrettably, we find considerable evidence in support of the detractors of ABM as a scientific enterprise: elementary plotting attributes are left off more often than not; basic information such as the number of replicates or the basis behind a particular statistic are not included; and few, if any, established methodological communication standards are apparent. In short, 'anarchy reigns'. Whilst the study was confined only to ABM papers of JASSS, we conclude that if the ABM community wishes its approach to be accepted further afield, authors, reviewers, and editors should take the results of our work as a wake-up call.

Exploring the Combined Effect of Factors Influencing Commuting Patterns and CO2 Emissions in Aberdeen Using an Agent-Based Model

Jiaqi Ge and J. Gareth Polhill
Journal of Artificial Societies and Social Simulation 19 (3) 11

Abstract: This paper develops an agent-based model of the daily commute in Aberdeen City and the surrounding area in Scotland, UK. We study the impact of flexitime work arrangements, urban concentration, a new bypass, and cycle lanes on commute time length, reliability and CO2 emissions, and analyse the diverse conflation of these factors and the different connections of them in order to detect their cumulative effects. Our results suggest that flexitime will reduce CO2 emissions from traffic. It also reduces mean commute time and makes commute time more reliable. We find that although higher urban concentration will make travel time less reliable, it will reduce CO2 emissions from commuting and cut commute time length. There might also be a trade-off between travel time length and reliability regarding urban concentration. We show that the new bypass will only reduce mean commute time by a small amount, while slightly increasing total CO2 emissions. Finally, we find that cyclists sharing roads with cars do not necessarily slow down the traffic on the whole. We conclude that infrastructural, social and urban issues should never be studied in isolation with each other, and that urban policies will have ramifications for both urban and surrounding ex-urban areas.

Recent Development of Social Simulation as Reflected in JASSS Between 2008 and 2014: A Citation and Co-Citation Analysis

Jonas Hauke, Iris Lorscheid and Matthias Meyer
Journal of Artificial Societies and Social Simulation 20 (1) 5

Abstract: The research field of social simulation comprises many topics and research directions. A previous study about the early years indicated that the community has evolved into a differentiated discipline. This paper investigates the recent development of social simulation as reflected in Journal of Artificial Societies and Social Simulation (JASSS) publications from 2008 to 2014. By using citation analysis, we identify the most influential publications and study the characteristics of citations. Additionally, we analyze the development of the field with respect to research topics and their structure in a co-citation analysis. The citation characteristics support the continuing highly multidisciplinary character of JASSS. Prominently cited are methodological papers and books, standards, and NetLogo as the main simulation tool. With respect to the focus of this research, we observe continuity in topics such as opinion dynamics and the evolution of cooperation. While some topics disappeared such as learning, new subjects emerged such as marriage formation models and tools and platforms. Overall, one can observe a maturing inter- and multidisciplinary scientific community in which both methodological issues and specific social science topics are discussed and standards have emerged.

Experiments with a Model of Domestic Energy Demand

Nicholas M. Gotts and J. Gareth Polhill
Journal of Artificial Societies and Social Simulation 20 (3) 11

Abstract: The CEDSS-3.4 agent-based model of domestic energy demand at community level is described. CEDSS (Community Energy Demand Social Simulator) is focused on household decisions (the model’s agents are households) to buy energy-using appliances, heating systems, and insulation, over the period from 2000 to 2049. Its empirical basis is a survey of households in Aberdeen and Aberdeenshire, Scotland, carried out in 2010, combined with publicly available data on household finances and equipment, and energy prices. CEDSS-3.4 emphasises mechanisms concerning value-strength dynamics and goal selection which influence such decisions, drawing on goal-framing theory. Results of experiments with the model are presented; the most important parameters for determining energy demand turn out to be economic (rates of change of incomes and of fuel prices), and the presence or absence of external (extra-community) influences on value-strengths. However, the value-strength dynamics used led in most runs to a single set of values dominating the population by 2049 – but even with identical parameters, different sets of values could become dominant, and which did so made a very considerable difference to demand. This resulted in bimodal distributions of outcome measures across the runs using a given parameter-setting in many cases; initial experiments indicated that changing parameters determining how far households influence each others’ values could at least reduce this tendency. Issues in the analysis of complex models with aspects unconstrained by either data or theory are discussed in the final section.

One Theory - Many Formalizations: Testing Different Code Implementations of the Theory of Planned Behaviour in Energy Agent-Based Models

Hannah Muelder and Tatiana Filatova
Journal of Artificial Societies and Social Simulation 21 (4) 5

Abstract: As agent-based modelling gains popularity, the demand for transparency in underlying modelling assumptions grows. Behavioural rules guiding agents' decisions, learning, interactions and possible changes in these should rely on solid theoretical and empirical grounds. This field has matured enough to reach the point at which we need to go beyond just reporting what social theory we base these rules upon. Many social science theories operate with various abstract constructs such as attitudes, perceptions, norms or intentions. These concepts are rather subjective and remain open to interpretation when operationalizing them in a formal model code. There is a growing concern that how modellers interpret qualitative social science theories in quantitative ABMs may differ from case to case. Yet, formal tests of these differences are scarce, and a systematic approach to analyse any possible disagreements is lacking. Our paper addresses this gap by exploring the consequences of variations in formalizations of one social science theory on the simulation outcomes of agent-based models of the same class. We ran simulations to test the impact of four types of differences: in model architecture concerning specific equations and their sequence within one theory, in factors affecting agents' decisions, in the representation of these potentially different factors, and finally in the underlying distribution of data used in a model. We illustrate emergent outcomes of these differences using the example of an agent-based model, which is developed to study regional impacts of households' solar panel investment decisions. The Theory of Planned Behaviour was applied as one of the most common social science theories used to define behavioural rules of individual agents. Our findings demonstrate qualitative and quantitative differences in the simulation outcomes, even when agents' decision rules are based on the same theory and data. The paper outlines a number of critical methodological implications for future developments in agent-based modelling.

Simulating the Actions of Commuters Using a Multi-Agent System

Neil Urquhart, Simon Powers, Zoe Wall, Achille Fonzone, Jiaqi Ge and J. Gareth Polhill
Journal of Artificial Societies and Social Simulation 22 (2) 10

Abstract: The activity of commuting to and from a place of work affects not only those travelling but also wider society through their contribution to congestion and pollution. It is desirable to have a means of simulating commuting in order to allow organisations to predict the effects of changes to working patterns and locations and inform decision making. In this paper, we outline an agent-based software framework that combines real-world data from multiple sources to simulate the actions of commuters. We demonstrate the framework using data supplied by an employer based in the City of Edinburgh UK. We demonstrate that the BDI-inspired decision-making framework used is capable of forecasting the transportation modes to be used. Finally, we present a case study, demonstrating the use of the framework to predict the impact of moving staff within the organisation to a new work site.

‘One Size Does Not Fit All’: A Roadmap of Purpose-Driven Mixed-Method Pathways for Sensitivity Analysis of Agent-Based Models

Arika Ligmann-Zielinska, Peer-Olaf Siebers, Nicholas Magliocca, Dawn C. Parker, Volker Grimm, Jing Du, Martin Cenek, Viktoriia Radchuk, Nazia N. Arbab, Sheng Li, Uta Berger, Rajiv Paudel, Derek T. Robinson, Piotr Jankowski, Li An and Xinyue Ye
Journal of Artificial Societies and Social Simulation 23 (1) 6

Abstract: Designing, implementing, and applying agent-based models (ABMs) requires a structured approach, part of which is a comprehensive analysis of the output to input variability in the form of uncertainty and sensitivity analysis (SA). The objective of this paper is to assist in choosing, for a given ABM, the most appropriate methods of SA. We argue that no single SA method fits all ABMs and that different methods of SA should be used based on the overarching purpose of the model. For example, abstract exploratory models that focus on a deeper understanding of the target system and its properties are fed with only the most critical data representing patterns or stylized facts. For them, simple SA methods may be sufficient in capturing the dependencies between the output-input spaces. In contrast, applied models used in scenario and policy-analysis are usually more complex and data-rich because a higher level of realism is required. Here the choice of a more sophisticated SA may be critical in establishing the robustness of the results before the model (or its results) can be passed on to end-users. Accordingly, we present a roadmap that guides ABM developers through the process of performing SA that best fits the purpose of their ABM. This roadmap covers a wide range of ABM applications and advocates for the routine use of global methods that capture input interactions and are, therefore, mandatory if scientists want to recognize all sensitivities. As part of this roadmap, we report on frontier SA methods emerging in recent years: a) handling temporal and spatial outputs, b) using the whole output distribution of a result rather than its variance, c) looking at topological relationships between input data points rather than their values, and d) looking into the ABM black box – finding behavioral primitives and using them to study complex system characteristics like regime shifts, tipping points, and condensation versus dissipation of collective system behavior.

The ODD Protocol for Describing Agent-Based and Other Simulation Models: A Second Update to Improve Clarity, Replication, and Structural Realism

Volker Grimm, Steven F. Railsback, Christian E. Vincenot, Uta Berger, Cara Gallagher, Donald L. DeAngelis, Bruce Edmonds, Jiaqi Ge, Jarl Giske, Jürgen Groeneveld, Alice S.A. Johnston, Alexander Milles, Jacob Nabe-Nielsen, J. Gareth Polhill, Viktoriia Radchuk, Marie-Sophie Rohwäder, Richard A. Stillman, Jan C. Thiele and Daniel Ayllón
Journal of Artificial Societies and Social Simulation 23 (2) 7

Abstract: The Overview, Design concepts and Details (ODD) protocol for describing Individual- and Agent-Based Models (ABMs) is now widely accepted and used to document such models in journal articles. As a standardized document for providing a consistent, logical and readable account of the structure and dynamics of ABMs, some research groups also find it useful as a workflow for model design. Even so, there are still limitations to ODD that obstruct its more widespread adoption. Such limitations are discussed and addressed in this paper: the limited availability of guidance on how to use ODD; the length of ODD documents; limitations of ODD for highly complex models; lack of sufficient details of many ODDs to enable reimplementation without access to the model code; and the lack of provision for sections in the document structure covering model design rationale, the model’s underlying narrative, and the means by which the model’s fitness for purpose is evaluated. We document the steps we have taken to provide better guidance on: structuring complex ODDs and an ODD summary for inclusion in a journal article (with full details in supplementary material; Table 1); using ODD to point readers to relevant sections of the model code; update the document structure to include sections on model rationale and evaluation. We also further advocate the need for standard descriptions of simulation experiments and argue that ODD can in principle be used for any type of simulation model. Thereby ODD would provide a lingua franca for simulation modelling.

Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action

Flaminio Squazzoni, J. Gareth Polhill, Bruce Edmonds, Petra Ahrweiler, Patrycja Antosz, Geeske Scholz, Émile Chappin, Melania Borit, Harko Verhagen, Francesca Giardini and Nigel Gilbert
Journal of Artificial Societies and Social Simulation 23 (2) 10

Abstract: The COVID-19 pandemic is causing a dramatic loss of lives worldwide, challenging the sustainability of our health care systems, threatening economic meltdown, and putting pressure on the mental health of individuals (due to social distancing and lock-down measures). The pandemic is also posing severe challenges to the scientific community, with scholars under pressure to respond to policymakers’ demands for advice despite the absence of adequate, trusted data. Understanding the pandemic requires fine-grained data representing specific local conditions and the social reactions of individuals. While experts have built simulation models to estimate disease trajectories that may be enough to guide decision-makers to formulate policy measures to limit the epidemic, they do not cover the full behavioural and social complexity of societies under pandemic crisis. Modelling that has such a large potential impact upon people’s lives is a great responsibility. This paper calls on the scientific community to improve the transparency, access, and rigour of their models. It also calls on stakeholders to improve the rapidity with which data from trusted sources are released to the community (in a fully responsible manner). Responding to the pandemic is a stress test of our collaborative capacity and the social/economic value of research.

Where Does Theory Have It Right? A Comparison of Theory-Driven and Empirical Agent Based Models

Firouzeh Taghikhah, Tatiana Filatova and Alexey Voinov
Journal of Artificial Societies and Social Simulation 24 (2) 4

Abstract: Computational social science has witnessed a shift from pure theoretical to empirical agent-based models (ABMs) grounded in data-driven correlations between behavioral factors defining agents' decisions. There is a strong urge to go beyond theoretical ABMs with behavioral theories setting stylized rules that guide agents' actions, especially when it concerns policy-related simulations. However, it remains unclear to what extent theory-driven ABMs mislead, if at all, a choice of a policy when compared to the outcomes of models with empirical micro-foundations. This is especially relevant for pro-environmental policies that increasingly rely on quantifying cumulative effects of individual behavioral changes, where ABMs are so helpful. We propose a comparison framework to address this methodological dilemma, which quantitatively explores the gap in predictions between theory- and data-driven ABMs. Inspired by the existing theory-driven model, ORVin-T, which studies the individual choice between organic and conventional products, we design a survey to collect data on individual preferences and purchasing decisions. We then use this extensive empirical microdata to build an empirical twin, ORVin-E, replacing the theoretical assumptions and secondary aggregated data used to parametrize agents' decision strategies with our empirical survey data. We compare the models in terms of key outputs, perform sensitivity analysis, and explore three policy scenarios. We observe that the theory-driven model predicts the shifts to organic consumption as accurately as the ABM with empirical micro-foundations at both aggregated and individual scales. There are slight differences (±5%) between the estimations of the two models with regard to different behavioral change scenarios: increasing conventional tax, launching organic social-informational campaigns, and their combination. Our findings highlight the goodness of fit and usefulness of theoretical modeling efforts, at least in the case of incremental behavioral change. It sheds light on the conditions when theory-driven and data-driven models are aligned and on the value of empirical data for studying systemic changes.

Using Agent-Based Models for Prediction in Complex and Wicked Systems

J. Gareth Polhill, Matthew Hare, Tom Bauermann, David Anzola, Erika Palmer, Doug Salt and Patrycja Antosz
Journal of Artificial Societies and Social Simulation 24 (3) 2

Abstract: This paper uses two thought experiments to argue that the complexity of the systems to which agent-based models (ABMs) are often applied is not the central source of difficulties ABMs have with prediction. We define various levels of predictability, and argue that insofar as path-dependency is a necessary attribute of a complex system, ruling out states of the system means that there is at least the potential to say something useful. ‘Wickedness’ is argued to be a more significant challenge to prediction than complexity. Critically, however, neither complexity nor wickedness makes prediction theoretically impossible in the sense of being formally undecidable computationally-speaking: intractable being the more apt term given the exponential sizes of the spaces being searched. However, endogenous ontological novelty in wicked systems is shown to render prediction futile beyond the immediately short term.