Communication plays a pivotal role in social phenomena such as belief polar- ization, scientific inquiry, and collective problem-solving. Agent-Based Models (ABMs) are computational tools that simulate the emergence of macro-level phenomena from micro-level interactions among agents. This paper focuses on Argumentative Agent-Based Models (AABMs), a specialized subset of ABMs that study argumentative communication, where agents provide reasons to sup- port or counter opinions. We present a systematic overview of AABMs, detailing their design, methodologies, and applications across (...) disciplines. Key research questions include understanding the dynamics of consensus versus polarization, the conditions for epistemic reliability in collective decision-making, and the mechanisms that foster efficient collaboration within diverse groups through ar- gumentative exchanges. By synthesizing contributions from computer science, social science, and philosophy, this paper serves as both an entry point for new- comers and a comprehensive resource for researchers advancing the study of AABMs. (shrink)

Now that complex Agent-Based Models and computer simulations spread over economics and social sciences - as in most sciences of complex systems -, epistemological puzzles (re)emerge. We introduce new epistemological tools so as to show to what precise extent each author is right when he focuses on some empirical, instrumental or conceptual significance of his model or simulation. By distinguishing between models and simulations, between types of models, between types of computer simulations and between types of empiricity, section (...) 2 gives conceptual tools to explain the rationale of the diverse epistemological positions presented in section 1. Finally, we claim that a careful attention to the real multiplicity of denotational powers of symbols at stake and then to the implicit routes of references operated by models and computer simulations is necessary to determine, in each case, the proper epistemic status and credibility of a given model and/or simulation. (shrink)

This study provides a basic introduction to agent-based modeling (ABM) as a powerful blend of classical and constructive mathematics, with a primary focus on its applicability for social science research. The typical goals of ABM social science researchers are discussed along with the culture-dish nature of their computer experiments. The applicability of ABM for science more generally is also considered, with special attention to physics. Finally, two distinct types of ABM applications are summarized in order to illustrate (...) concretely the duality of ABM: Real-world systems can not only be simulated with verisimilitude using ABM; they can also be efficiently and robustly designed and constructed on the basis of ABM principles. (shrink)

Agent-based modeling has become a common and well-established tool in the social sciences and certain of the humanities. Here, we aim to provide an overview of the different modeling approaches in current use. Our discussion unfolds in two parts: we first classify different aspects of the model-building process and identify a number of characteristics shared by most agent-based models in the humanities and social sciences; then we map relevant differences between the various modeling (...) approaches. We classify these into different dimensions including the type of target systems addressed, the intended modeling goals, and the models’ degree of abstraction. Along the way, we provide reference to related debates in contemporary philosophy of science. (shrink)

This book contains a selection of papers that have been presented at the workshop 'Recent Developments and future directions in agent-based modelling in population studies' that was organized at tha University of Leuven (KU Leuven) Belgium, 18-19 September 2014.

This book brings together contributions from leading researchers in the field of agent-based modelling and simulation. This approach has grown out of some recent and innovative ideas in the social sciences, computer sciences, life sciences, physics and game theory. It is proving helpful in understanding complexity in many domains. The opportunities it offers to explore the experimental approach to social and human behaviour is proving of theoretical and empirical value across a wide range of fields. With contributions from (...) researchers whose work has served to define this new field such as Nigel Gilbert, Robert Axtell (in economics and social science) and Jacques Ferber (in multi-agent systems), as well as practitioners who are working at the cutting edge of the new domain, this collection of essays has been assembled by two of its leading exponents: Frédéric Amblard and Denis Phan. The research, case studies and theoretical approaches discussed in this book are designed to introduce beginners and experts alike to the current state of play in this new and exciting field of social science. (shrink)

This volume brings together contributions from leading researchers in the field of agent-based modelling and simulation. This approach has grown out of some recent and innovative ideas in the social sciences, computer sciences, life sciences, physics and game theory. It is proving helpful in understanding complexity in many domains. The opportunities it offers to explore the experimental approach to social and human behaviour is proving of theoretical and empirical value across a wide range of fields. With contributions from (...) researchers whose work has served to define this new field such as Nigel Gilbert, Robert Axtell (in economics and social science) and Jacques Ferber (in multi-agent systems), as well as practitioners who are working at the cutting edge of the new domain, this collection of essays has been assembled by two of its leading exponents: Frédéric Amblard and Denis Phan. The research, case studies and theoretical approaches discussed in this book are designed to introduce beginners and experts alike to the current state of play in this new and exciting field of social science. (shrink)

Agent-based models (ABMs) are one type of simulation model used in the context of the COVID-19 pandemic. In contrast to equation-based models, ABMs are algorithms that use individual agents and attribute changing characteristics to each one, multiple times during multiple iterations over time. This paper focuses on three philosophical aspects of ABMs as models of causal mechanisms, as generators of emergent phenomena, and as providers of explanation. Based on my discussion, I conclude that while ABMs cannot (...) help much with causal inference, they can be viewed as etio-prognostic explanations of illness occurrence and outcome. (shrink)

In this paper, we present an agent-based model for studying the impact of 'myside bias' on the argumentative dynamics in scientific communities. Recent insights in cognitive science suggest that scientific reasoning is influenced by `myside bias'. This bias manifests as a tendency to prioritize the search and generation of arguments that support one's views rather than arguments that undermine them. Additionally, individuals tend to apply more critical scrutiny to opposing stances than to their own. Although myside bias may (...) pull individual scientists away from the truth, its effects on communities of reasoners remain unclear. The aim of our model is two-fold: first, to study the argumentative dynamics generated by myside bias, and second, to explore which mechanisms may act as a mitigating factor against its pernicious effects. Our results indicate that biased communities are epistemically less successful than non-biased ones, and that they also tend to be less polarized than non-biased ones. Moreover, we find that two socio-epistemic mechanisms help communities to mitigate the effect of the bias: the presence of a common filter on weak arguments, which can be interpreted as shared beliefs, and an equal distribution of agents for each alternative at the beginning. (shrink)

Agent-​​based modeling is showing great promise in the social sciences. However, two misconceptions about the relation between social macroproperties and microproperties afflict agent-based models. These lead current models to systematically ignore factors relevant to the properties they intend to model, and to overlook a wide range of model designs. Correcting for these brings painful trade-​​offs, but has the potential to transform the utility of such models.

Herein is provided a brief overview of agent-based modeling , with a particular slant toward its potential as a tool for the theoretical biologist. Of course, its use for experimentalists is obvious; they are, as a group, already using agent-based models to examine contemporary biological questions ranging from molecular processes to evolving ecological landscapes . For the modeler-theoretician, ABMs offer a new tool for thinking about the structure of biological processes, thus creating a conduit for (...) integrative perspective taking between the experimentalist and the theoretician. (shrink)

Agent-based modeling (ABM) is a simulation technique which has been increasingly integrated into the economic discipline in order to understand complex systems. However, most of everyday research activities rely on the researchers' consensus concerning practical choices about modeling strategies, computational boundaries under scrutiny and the extent of empirical validation. Particularly lacking are reflections on the semantic construction of conceptual models. The paper reviews existing theoretical frameworks leading to the understanding of ABM as a technique where the (...) cognitive processing instantiated by the instrument is distributed across different modeling layers, including conceptual, algorithmic and computational. These layers can be interpreted as an interlinked set of analogies. Then, the paper introduces a framework for assessing ABM conceptual adequacy and tests it on two families of models in the economics of innovation field. (shrink)

Agent-based models in philosophy of science.

Frigg and Reiss (2009) argue that philosophical problems in simulation bear enough resemblance to recognized issues in the philosophy of modeling that they only pose challenges analogous to those found in standard analytic models used to represent natural systems. They suggest that there are no new philosophical problems in computer simulation modeling beyond those found in traditional mathematical modeling. Winsberg (2009) has countered that there appear to be genuinely new epistemological problems in simulation modeling because the (...) knowledge obtained from them is ‘downward, motley, and autonomous.’ Here I draw out some specific ways that these epistemological problems are manifest in complex ecological simulation, especially in agent-based models. These models contain novel features that were impossible to anticipate prior to the computer revolution, and continue to present difficulties and challenges of both a practical and philosophical nature (Humphreys 2002). (shrink)

Agent-based models have played a prominent role in recent debates about the merits of democracy. In particular, the formal model of Lu Hong and Scott Page and the associated “diversity trumps ability” result has typically been seen to support the epistemic virtues of democracy over epistocracy (i.e., governance by experts). In this paper we first identify the modeling choices embodied in the original formal model and then critique the application of the Hong-Page results to philosophical debates on (...) the relative merits of democracy. In particular we argue that the “best-performing agents” in Hong-Page model should not be interpreted as experts. We next explore a closely related model in which best-performing agents are more plausibly seen as experts and show that the diversity trumps ability result fails to hold. However, with changes in other parameters (such as the deliberation dynamic) the diversity trumps ability result is restored. The sensitivity of this result to parameter choices illustrates the complexity of the link between formal modeling and more general philosophical claims; we use this debate as a platform for a more general discussion of when and how agent-based models can contribute to philosophical discussions. (shrink)

The article presents an agent-based model (ABM) of scientific interaction aimed at examining how different degrees of connectedness of scientists impact their efficiency in knowledge acquisition. The model is built on the basis of Zollman’s ( [2010] ) ABM by changing some of its idealizing assumptions that concern the representation of the central notions underlying the model: epistemic success of the rivalling scientific theories, scientific interaction and the assessment in view of which scientists choose theories to work on. (...) Our results suggest that whether and to what extent the degree of connectedness of a scientific community impacts its efficiency is a highly context-dependent matter since different conditions deem strikingly different results. More generally, we argue that simplicity of ABMs may come at a price: the requirement to run extensive robustness analysis before we can specify the adequate target phenomenon of the model.1 1 1Introduction 2Zollman's 2010 Model 3Static versus Dynamic Epistemic Success 3.1Introducing the notion of dynamic epistemic success 3.2Implementation and results for the basic setup 4Critical Interaction 4.1Introducing critique 4.2Implementation and results 5Inertia of Inquiry 5.1Introducing rational inertia 5.2Implementation and results 6Threshold Below Which Theories Are Equally Promising 6.1An inquiry that is even more difficult 6.2Implementation and results 7Discussion 8Conclusion. (shrink)

Agent-Based Models are useful to describe and understand social, economic and spatial systems' dynamics. But, beside the facilities which this methodology offers, evaluation and comparison of simulation models are sometimes problematic. A rigorous conceptual frame needs to be developed. This is in order to ensure the coherence in the chain linking at the one extreme the scientist's hypotheses about the modeled phenomenon and at the other the structure of rules in the computer program. This also systematizes the model (...) design from the thematician conceptual framework as well. The aim is to reflect upon the role that a well defined ontology, based on the crossing of the philosophical and the computer science insights, can play to solve such questions and help the model building. We analyze different conceptions of ontology, introduce the 'ontological test' and show its usefulness to compare models. Then we focus on the model building and show the place of a systematic ABM ontology. The latter process is situated within a larger framework called the 'knowledge framework' in which not only the ontologies but also the notions of theory, model and empirical data take place. At last the relation between emergence and ontology is discussed. (shrink)

Agent-based models (ABMs) are widely used to study the complex dynamics and emergent properties of systems with many interacting agents. This includes belief and opinion dynamics as are of relevance to understanding contexts as varied as online social media and the practice of science. This paper argues that such ABMs can capture rich argumentation scenarios in ways that have not been covered in research to date. To clarify the space of potential agent-based models of argument, we (...) distinguish three interrelated notions of argument from the literature. First, arguments as reasons refer simply to the propositional content encoded in arguments. Second, arguments as syllogism describe premise-conclusion relationships that arise between such reasons when asserted as arguments. Third, arguments as dialectics refer to the deployment of reasons and syllogisms in discussions (be they polylogues or dialogues). We show how modelling each of these three notions of argument naturally involves a continuum of complexity. Specifically, we use the NormAN framework (introduced in Assaad et al. A Bayesian agent-based framework for argument exchange across networks. https://doi.org/10.48550/arXiv.2311.09254, 2023), which bases ABMs on the theory of Bayesian networks, as a point of reference and draw out its relationship to other modelling frameworks along each of these dimensions. This provides a novel organising scheme to aid model comparison and model choice, and clarifies ways in which these three notions of argument constrain one another. This shows also that NormAN’s Bayesian framework not only captures familiar facets of argumentation, but also allows one to study how dialectical considerations influence population level diffusion of arguments (as we demonstrate with a small simulation study). (shrink)

Scientific research that could cause grave harm, either through accident or intentional malevolence, is known as dual-use research. Recent high-profile cases of dual-use research in the life sciences have led to debate about the extent to which restrictions on the conduct and dissemination of such research may impede scientific progress. We adapt formal models of scientific networks to systematically explore the effects that different regulatory schemes may have on a community’s ability to learn about the world. Our results suggest that, (...) contrary to common wisdom, some restrictions on the conduct and dissemination of dual-use research do not inhibit scientific progress and may actually aid communities in learning. (shrink)

In this paper I examine the epistemic function of agent-based models of scientific inquiry, proposed in the recent philosophical literature. In view of Boero and Squazzoni’s classification of ABMs into case-based models, typifications and theoretical abstractions, I argue that proposed ABMs of scientific inquiry largely belong to the last category. While this means that their function is primarily exploratory, I suggest that they are epistemically valuable not only as a temporary stage in the development of ABMs of (...) science, but by providing insights into theoretical aspects of scientific rationality. I illustrate my point with two examples of highly idealized ABMs of science, which perform two exploratory functions: Zollman’s ABM which provides a proof-of-possibility in the realm of theoretical discussions on scientific rationality, and an argumentation-based ABM, which provides insights into potential mechanisms underlying the efficiency of scientific inquiry. (shrink)

We discuss how to define “agents” and “objects” for agent-based modeling in economics (ABM) using the principles of philosophical trope theory and then propose a set of definitions for creating a framework where the tropes could be used as the objects of computation when creating models for ABM in a way that the agents can be considered as “trope processors”. We establish sets of assumptions to characterize agents and objects with tropes and we discuss the assumptions, how (...) economic objects and the values, which the agents assign to them, can be described as mereological sums of the objects’ properties and their corresponding valuations within the trope theory framework. We also discuss the benefits of the trope theory approach in general, for example, how to clarify the ontology of conceptualizing models for object-oriented programming and how the agent-based model, based on trope theory, could be constructed in practice. (shrink)

Simulation of complex biological systems with agent-based models is becoming more relevant with the increase in Graphics Processing Unit power. In those simulations, up to millions of virtual cells are individually computed, involving daunting processing times. An important part of computational models is the algorithm that manages how agents perceive their surroundings. This can be particularly problematic in three-dimensional environments where agents have deformable virtual membranes. This article presents a GPU algorithm that gives the possibility for agents to (...) integrate the signals scattered on their virtual membrane. It is detailed to be coded in languages like OpenCL or Cuda. Its performances are tested to show its speed with current GPU devices. Finally, it was implemented inside an existing software to test and illustrate the possibilities it offers. (shrink)

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S (...) requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework—a dynamic knowledge repository—wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline. (shrink)

This article describes the potential impact that free (i.e., open source) software can have on an existing commercial software market. A model for the software market is constructed in terms of autonomous agents, which represent the users, the companies, and the free software providers. The model specifies a reservation price for each user agent and develops a gradient learning strategy for revenue-maximizing company agents. Simulations explore parameters such as the demand distribution, and the relative importance of market share, advertising (...) and random effects in product visibility. Results from the case without free software show a prevalence of monopolies, which is consistent with other studies of high-technology market economics. The effects of free software are not uniform, but are highly parameter dependent. A “capture region” is found in which free software eventually dominates the market. (shrink)

In this paper we examine the epistemic value of highly idealized agent-based models of social aspects of scientific inquiry. On the one hand, we argue that taking the results of such simulations as informative of actual scientific inquiry is unwarranted, at least for the class of models proposed in recent literature. Moreover, we argue that a weaker approach, which takes these models as providing only “how-possibly” explanations, does not help to improve their epistemic value. On the other hand, (...) we suggest that if ABMs of science underwent two types of robustness analysis, they could indeed have a clear epistemic function, namely by providing evidence for philosophical and historical hypotheses. In this sense, ABMs can obtain evidential and explanatory properties and thus be a useful tool for integrated history and philosophy of science. We illustrate our point with an example of a model—building on the work by Kevin Zollman—which we apply to a concrete historical case study. (shrink)

Building mechanisms-based, black box–free explanations is the main goal of analytical sociology. In this article, I offer some reasons to question whether some of the conceptual and methodological developments of the analytical community really serve this goal. Specifically, I argue that grounding our computer modeling practices in the current definition of mechanisms posits a serious risk of defining an ideal-typical research path that neglects the role that the understanding of the generative process must have for a black box–free (...) explanation to be met. I propose some conceptual and methodological alternatives, and I identify some collective challenges that the analytical community should tackle in order not to deviate from its main goal. (shrink)

Agent-based models (ABMs) are one of the main sources of evidence for decisions regarding mitigation and suppression measures against the spread of SARS-CoV-2. These models have not been previously included in the hierarchy of evidence put forth by the evidence-based medicine movement, which prioritizes those research methods that deliver results less susceptible to the risk of confounding. We point out the need to assess the quality of evidence delivered by ABMs and ask the question of what is (...) the risk that assumptions entertained in ABMs do not include all the key factors and make model predictions susceptible to the problem of confounding. (shrink)

Since Economics survey by Windrum et al., research on empirical validation of agent-based Agent-based model in Economics has made substantial advances, thanks to a constant flow of high-quality contributions. This Chapter attempts to take stock of such recent literature to offer an updated critical review of the existing validation techniques. We sketch a simple theoretical framework that conceptualizes existing validation approaches, which we examine along three different dimensions: Comparison between artificial and real-world Data; Calibration and estimation (...) of model parameters; and parameter space Exploration. Finally, we discuss open issues in the field of ABM validation and Estimation. In particular, we argue that more research efforts should be devoted toward advancing HypothesisTesting in ABM, with specific emphasis on model stationarity and ergodicity. (shrink)

This article aims at contributing to the discussion about the relationships between ICT, computer science and policy-making by focusing on agent-based social simulation. Enabled, from a technical point of view, by the developments of Distributed Artificial Intelligence in the 1990s and by the features of the object-oriented programming paradigm, agent-based social simulations are a tool for the analysis of social dynamics that can be used also to support the design and the evaluation of public policies. After (...) a brief description of social simulation paradigm and of its impact on social sciences, the paper presents a simple agent-based model devised to analyze, even if in a very abstract way, a phenomenon that can rouse the interest of legal scientists: the interplay between damaging behaviors, punishment and social mechanisms of learning and imitation. Our goal is to show how agent-based simulation can be used not only to illuminate basic mechanisms underlying social phenomena but also to reflect, in an innovative way, on how society can deal with them. (shrink)

This open access book examines the methodological complications of using complexity science concepts within the social science domain. The opening chapters take the reader on a tour through the development of simulation methodologies in the fields of artificial life and population biology, then demonstrates the growing popularity and relevance of these methods in the social sciences. Following an in-depth analysis of the potential impact of these methods on social science and social theory, the text provides substantive examples of the application (...) of agent-based models in the field of demography. This work offers a unique combination of applied simulation work and substantive, in-depth philosophical analysis, and as such has potential appeal for specialist social scientists, complex systems scientists, and philosophers of science interested in the methodology of simulation and the practice of interdisciplinary computing research.​. (shrink)

Establishing credibility and trustworthiness is essential in Economic Agent-Based Modeling (ABM), where clear epistemic standards cannot be defined a priori. In this paper, we first review the notions of trustworthiness and credibility in modeling. We then introduce a framework that emphasizes the modeler's epistemic responsibility to ensure coherence between modeling purposes, strategies and targets. We examine the challenges in assessing model reliability that arise from the interaction of conceptual, algorithmic and computational constituents, and we propose (...) a meta-analytical approach to enhance model consistency by conceptualizing agent-based simulations as iterated analogies. Our analysis outlines strategies for improving model accessibility and reliability while highlighting the modeler's role in preventing mistargeting and misuse. This research provides a normative basis for justifying the credibility of both idealized and targetless models by promoting transparency and consistency between model design and intended purposes. (shrink)

This paper is a summary of the research developed in the author’s Ph.D. programme. The case study deals with the implementation of Energy Efficiency policies in the Building sector of the Lazio Region and carries out an ABM analysis of the impacts of training on the social actors involved. Its purpose is to reproduce the social mechanisms through the study of the actors’ actions with ABM in order to evaluate socio-economic impacts by interconnecting the social and economic variables by means (...) of the Social Accounting Matrix. (shrink)

Nicholas Vriend asked whether F.A. Hayek was an “ace,” and answered affirmatively. By “ace,” Vriend meant someone who worked with agent-based modeling. To be sure, Hayek could not have worked with agent-based models because that platform did not exist when Hayek was developing his ideas about the distribution and use of knowledge in society. All the same, Vriend explained convincingly that Hayek could have made good use of the agent-based platform had it been (...) available in the 1930s. To similar effect, we argue in this paper that James Buchanan could have made good use of agent-based modeling to carry forward his ideas about the value of taking an emergent approach to the explanation of economic processes. (shrink)

Neoclassical economic theory, which still dominates the science, has proven inadequate to predict financial crises. In an increasingly globalised world, the consequences of that inadequacy are likely to become more severe. This article attributes much of the difficulty to an emphasis on equilibrium as an idealised property of economic systems. Alternatively, this article proposes that actual economies are typically out of balance, and that any equilibrium which may exist is transitory. That single changed assumption is central to complexity economics, a (...) view which is presented in detail. It is suggested that economic crises will be most effectively avoided when economists utilise methods, grounded in complexity theory, which can identify threat in an early stage. As a programmatic example, the use of Agent-Based Models (ABMs) combined with Boolean networks (BNs), is defended as a promising method for recognising vulnerability. (shrink)

In this paper, we describe a framework for studying social agents’ individual decision making, that takes account of the environment and social dynamics. We describe a study in which we explored the efficiency of foraging strategies within a group of individuals faced with a resource-limited environment. We investigated to what extent cooperative and non-cooperative behaviors impacted on the survival rates of a population of individuals. In the experiment presented here, we considered two different types of individuals: selfish individuals who gather (...) energy for their own use, and cooperative individuals who share the energy they gather with others, thus reducing their own individual chances of survival. In order to study the trade-off between non-cooperative and cooperative behaviors in a pseudo-realistic two-dimensional environment, we introduced an agent-based modeling and simulation tool called ACACIA-ES, which simulated local interactions and spatial behavior for large numbers of individuals in complex environments. The main result from our simulation was that a group of cooperative individuals displayed better survival strategies than groups of selfish individuals when faced with a variety of environmental pressures; however, it was very unlikely that such cooperative strategies could resist competition from selfish individuals, if the outcome of past social interactions was memorized, even when a very small group of selfish individuals was introduced. (shrink)

Public reason liberalism includes an ideal of political stability where justified institutions reach a kind of self-enforcing equilibrium. Such an order must be stable for the right reasons — where persons comply with the rules of the order for moral reasons, rather than out of fear or self-interest. John Rawls called a society stable in this way well-ordered. In this essay, I contend that a more sophisticated model of a well-ordered society, specifically an agent-based model, yields a richer (...) and more attractive understanding of political stability. An agent-based model helps us to distinguish between three concepts of political stability — durability, balance, and immunity. A well-ordered society is one that possesses a high degree of social trust and cooperative behavior among its citizens (durability) with low short-run variability (balance). A well-ordered society also resists destabilization caused by noncompliant agents in or entering the system (immunity). Distinguishing between these three concepts complicates the necessary reformulation of the idea of a well-ordered society. Going forward, public reason theorists must now distinguish between types of assurance, specify heretofore unknown aspects of reasonable behavior, and reconceive of the nonideal preconditions for forming a stable, ideal social order. (shrink)

Agent-based models derive the behavior of artificial socio-economic entities computationally from the actions of a large number of agents. One objection is that highly idealized ABMs fail to represent the real world in any reasonable sense. Another objection is that they at best show how observed patterns may have come about, because simulations are easy to produce and there is no evidence that this is really what happens. Moreover, different models may well yield the same result. I will (...) rebut these objections by focusing on an often neglected, but crucial function of ABMs. Building on Gelfert’s account of the exploratory uses of scientific models I show that, in the absence of an accepted underlying theory, successful ABMs lend inductive support to assumptions concerning certain structural feutures of the behavioral rules employed. One core step towards this goal is what I call multiple-model robustness analysis. (shrink)

The emergence of bad beliefs – beliefs that blatantly conflict with readily available scientific evidence – has been attributed to two competing theories. One posits that people hold bad beliefs to gain social benefits from members of their social circle. The other points to a polluted epistemic environment that impairs people’s capacity to form accurate judgments. In this work, we compare the collective-level implications of both theories. Specifically, we use an agent-based simulation to explore the collective dynamics generated (...) by two models: one where beliefs are shaped by social incentives, and one where beliefs are only influenced by the epistemic environment. We find that in the first model – the social model, a stable minority of agents holding bad beliefs consistently emerges, regardless of initial conditions or network structure. By contrast, in the epistemic model, bad-belief clusters appear only in highly fragmented communities or when fanatics are present. We conclude by discussing the broader implications of these results for the debate concerning bad beliefs. (shrink)

Agent-based modelling has become a well-established method in social epistemology and philosophy of science but the question of what kind of explanations these models provide remains largely open. This paper is dedicated to this issue. It starts by distinguishing between real-world phenomena, real-world possibilities, and logical possibilities as different kinds of targets which agent-based models can represent. I argue that models representing the former two kinds provide how-actually explanations or causal how-possibly explanations. In contrast, models that (...) represent logical possibilities provide epistemically opaque how-possibly explanations. While highly idealised ABMs in the form in which they are initially proposed typically fall into the last category, the epistemic opaqueness of explanations they provide can be reduced by validation procedures. To this purpose, an examination of results of simulations in terms of classes of models can be particularly helpful. I illustrate this point by discussing a class of ABMs of scientific interaction and the claim that a high degree of interaction can impede scientific inquiry. (shrink)

This poster presents results from applying agent-based modeling to an exploration of risk attitudes and rational decision making in the context of group interaction. We are also interested in the place of agent-based modeling and computational philosophy within the computational humanities. Computational philosophy has not typically been included in Digital Humanities; computational work has been done using philosophy texts as a source for analysis (Kinney 2022; Malaterre et al. 2021; Fletcher et al. 2021; Zahorec (...) et al. 2022), but there are few examples of philosophical arguments being made based on computation (Zahorec et al. 2022). Modeling is largely accepted as a computational humanist method (McCarty 2004; So 2022), but typically models are based on data rather than designed simulations as a method analogous to a thought experiment (Mayo-Wilson / Zollman 2021). Agent-based modeling is not as common in computational humanities, but we are interested in its potential; for instance, see work modeling correspondence networks to better understand lost information and biases in correspondence data sets (Buarque / Vogl 2023), or work simulating past human societies (Romanowska et al. 2021). Our project explores Lara Buchak’s theory of risk-weighted rational decision making by extending it to the context of group populations and interactions. Buchak has proposed risk-weighted expected utility maximization, which incorporates individual risk attitudes into the standard expected utility (EU) calculation based on utility and probability of outcomes (Buchak 2017). This theory provides an explanation for differences of behavior seen in human populations, while still allowing those different choices to be rational. Buchak’s theory has previously only been applied to individual decision making; our work uses agent-based modeling to develop and analyze simulations which incorporate a variety of risk attitudes (risk avoidance and risk seeking) into game theoretic interactions. This project is a collaboration between a philosopher and a research software engineer; we have worked together to develop simulations that implement agents with risk attitudes making risky choices. This presentation focuses on results from an implementation of a hawk/dove game with multiple risk attitudes and risk attitude adjustment. In the hawk/dove game cooperation, or playing dove, is better for the population, but acting aggressively, or playing hawk, is better for an individual if they are in a neighborhood of doves. To set up the game, we place agents on a grid and have them play against each of their neighbors, and accumulate payoffs based on the success of their plays. We define risk attitudes 0 through 9, where that number corresponds to the minimum number of neighbors playing dove for an agent to play hawk. An agent with risk attitude 0 is most risk seeking and always plays hawk; an agent with risk attitude 1 will play hawk if at least one neighbor plays dove; an agent with risk attitude 9 always takes the safe choice and plays dove; an agent with risk attitude 4 or 5 is risk neutral (breaking ties in different ways), which corresponds to expected utility. In this simulation we randomly set agent initial risk attitudes, and then every ten rounds agents compare their payoffs with their neighbors and adopt the most successful risk attitude in their neighborhood. The simulation is coded to stop once risk attitude adjustments stabilize. We define 13 different population states based on primary and second majority of risk averse, risk inclined, or risk moderate, and analysis of over 1 million runs indicate that while it’s more likely to stabilize with the population having become majority risk inclined, that is not the only outcome; we also see cases where the population stabilizes as risk neutral, risk avoidant, and many cases with no clear majority. This diversity of stable populations provides support for the argument that no one risk attitude is uniquely rational, but rather that risk attitudes are conventional. This matches what we observe in the real world: that different populations have different risk attitudes. Our simulations include numerous parameters to vary grid size; adjustment frequency, strategy, and wealth comparison; neighborhood sizes for observing previous choice, playing against, and comparing payoffs for adjusting risk attitudes; and different distributions for initializing risk attitudes. Analysis of parameters and adjusted risk attitudes shows that the strongest correlations are the initial distribution of risk attitudes, which tend to be preserved. * * * This poster was accepted and presented at the DH2024 conference in Washington, D.C. (shrink)

The use of computer simulation for building theoretical models in social science is introduced. It is proposed that agent-based models have potential as a “third way” of carrying out social science, in addition to argumentation and formalisation. With computer simulations, in contrast to other methods, it is possible to formalise complex theories about processes, carry out experiments and observe the occurrence of emergence. Some suggestions are offered about techniques for building agent-based models and for debugging them. (...) A scheme for structuring a simulation program into agents, the environment and other parts for modifying and observing the agents is described. The article concludes with some references to modelling tools helpful for building computer simulations. (shrink)

The aim of this paper is to stress some ontological and methodological issues for Agent-Based Model (ABM) building, exploration, and evaluation in the Social and Human Sciences. Two particular domain of interest are to compare ABM and simulations (Model To Model) within a given academic field or across different disciplines and to use ontology for to discuss about the epistemic and methodological consequences of modeling choices. The paper starts with some definitions of ontology in philosophy and computer (...) sciences. The implicit and different ontology which underlies the approach of a same object of interest are discussed in the case of spatial economists and geographers. Finally, using the case of Shelling’s model, we discuss the concept of “ontological test,” and raise the question of the ontological compatibility between the “model world” and the “real world.”. (shrink)