 Subjects -> SOCIOLOGY (Total: 553 journals)
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- Special Section on "Inverse Generative Social Science": Guest
Editors’ Statement
Authors: je65@nyu.edu (Joshua M. Epstein; Ivan Garibay, Erez Hatna, Matthew Koehler William Rand
Abstract: Joshua M. Epstein, Ivan Garibay, Erez Hatna, Matthew Koehler and William Rand: This is a guest editors' statement accompanying the publication of a special issue on "Inverse Generative Social Science", published in volume 26, issue 2, 2023 of JASSS-Journal of Artificial Societies and Social Simulation"
PubDate: Fri, 31 Mar 2023 12:59:00 +000
- Inverse Generative Social Science: Backward to the Future
Authors: je65@nyu.edu (Joshua M. Epstein
Abstract: Joshua M. Epstein: The agent-based model is the principal scientific instrument of generative social science. Typically, we design completed agents—fully endowed with rules and parameters—to grow macroscopic target patterns from the bottom up. Inverse generative science (iGSS) stands this approach on its head: Rather than handcrafting completed agents to grow a target—the forward problem—we start with the macro-target and evolve micro-agents that generate it, stipulating only primitive agent-rule constituents and permissible combinators. Rather than specific agents as designed inputs, we are interested in agents—indeed, families of agents—as evolved outputs. This is the backward problem and tools from Evolutionary Computing can help us solve it. As the overarching essay in the current JASSS Special Section, Part 1 discusses the goals of iGSS as distinct from other approaches. Part 2 discusses how to do it concretely, previewing the five iGSS applications that follow. Part 3 discusses several foundational issues for agent-based modeling and economics. Part 4 proposes a central future application of iGSS: to evolve explicit formal alternatives to the Rational Actor, with Agent_Zero as one possible point of evolutionary departure. Conclusions and future research directions are offered in Part 5. Looking ‘backward to the future,’ I also include, as Appendices, a pair of 1992 memoranda to the then President of the Santa Fe Institute on the forward (growing artificial societies from the bottom up) and backward (iGSS) problems.
PubDate: Fri, 31 Mar 2023 12:58:00 +000
- Generating Mixed Patterns of Residential Segregation: An Evolutionary
Approach
Authors: gunaratnecs@ornl.gov (Chathika Gunaratne; Erez Hatna, Joshua M. Epstein Ivan Garibay
Abstract: Chathika Gunaratne, Erez Hatna, Joshua M. Epstein and Ivan Garibay: The Schelling model of residential segregation has demonstrated that even the slightest preference for neighbors of the same race can be amplified into community-wide segregation. However, these models are unable to simulate mixed, coexisting patterns of segregation and integration, which have been seen to exist in cities. Using evolutionary model discovery we demonstrate how including social factors beyond racial bias when modeling relocation behavior enables the emergence of strongly mixed patterns. Our results indicate that the emergence of mixed patterns is better explained by multiple factors influencing the decision to relocate; the most important being the interaction of nonlinear, rapidly diminishing racial bias with a recent, historical tendency to move. Additionally, preference for less isolated neighborhoods or preference for neighborhoods with longer residing neighbors may produce weaker mixed patterns. This work highlights the importance of exploring the influence of multiple hypothesized factors of decision making, and their interactions, within agent rules, when studying emergent outcomes generated by agent-based models of complex social systems.
PubDate: Fri, 31 Mar 2023 12:57:00 +000
- Can Social Norms Explain Long-Term Trends in Alcohol Use' Insights from
Inverse Generative Social Science
Authors: t.vu@sheffield.ac.uk (Tuong Manh Vu; Charlotte Buckley, João A. Duro, Alan Brennan, Joshua M. Epstein Robin C. Purshouse
Abstract: Tuong Manh Vu, Charlotte Buckley, João A. Duro, Alan Brennan, Joshua M. Epstein and Robin C. Purshouse: Social psychological theory posits entities and mechanisms that attempt to explain observable differences in behavior. For example, dual process theory suggests that an agent's behavior is influenced by intentional (arising from reasoning involving attitudes and perceived norms) and unintentional (i.e., habitual) processes. In order to pass the generative sufficiency test as an explanation of alcohol use, we argue that the theory should be able to explain notable patterns in alcohol use that exist in the population, e.g., the distinct differences in drinking prevalence and average quantities consumed by males and females. In this study, we further develop and apply inverse generative social science (iGSS) methods to an existing agent-based model of dual process theory of alcohol use. Using iGSS, implemented within a multi-objective grammar-based genetic program, we search through the space of model structures to identify whether a single parsimonious model can best explain both male and female drinking, or whether separate and more complex models are needed. Focusing on alcohol use trends in New York State, we identify an interpretable model structure that achieves high goodness-of-fit for both male and female drinking patterns simultaneously, and which also validates successfully against reserved trend data. This structure offers a novel interpretation of the role of norms in formulating drinking intentions, but the structure's theoretical validity is questioned by its suggestion that individuals with low autonomy would act against perceived descriptive norms. Improved evidence on the distribution of autonomy in the population is needed to understand whether this finding is substantive or is a modeling artefact.
PubDate: Fri, 31 Mar 2023 12:56:00 +000
- Evolutionary Model Discovery of Human Behavioral Factors Driving
Decision-Making in Irrigation Experiments
Authors: lux.miranda@it.uu.se (Lux Miranda; Ozlem O. Garibay Jacopo Baggio
Abstract: Lux Miranda, Ozlem O. Garibay and Jacopo Baggio: Small farms are thought to produce around a third of the global crop supply. But, in the wake of the climate crisis, their existence is increasingly vulnerable to changes in the spatial and temporal availability of water. The small-scale irrigation systems which water these farms present a social-ecological dilemma: Upstream farms have prevailing access to a canal's resources, but all farms along the canal must contribute to maintaining the irrigation infrastructure. Thus, it is key to assess the social mechanisms which promote resilience in these systems and, more widely, in complex social-ecological dilemmas under changing conditions. Toward this, we build on previous work in which a stylized irrigation dilemma was simulated via a social lab experiment. Studies of the data produced from this experiment modeled participants' behavior with multiple, theoretically grounded agent-based models (ABMs). These models encode causal, human-interpretable hypotheses of decision making which generates the real-world behavior observed in the experiment. However, the accuracy of these models in fitting the experimental data is limited. Using Evolutionary Model Discovery, a recent algorithm for inverse generative social science (iGSS), we show the ability to automatically generate a wide variety of unique new ABMs which fit the experimental data more accurately and robustly than the original, manually-constructed ABMs. To do this, we algorithmically explore the space of possible behavioral rules for agents choosing how to contribute to the maintenance of the irrigation infrastructure. We find that, in contrast to the original models, our best-performing models typically have an additional element of stochasticity and favor factors such as other-regarding preferences and perceived relative income. Given that this change in just a small part of the original model has yielded such an advance, our results suggest that iGSS methods have great potential for continuing to derive more accurate models of complex social-ecological dilemmas.
PubDate: Fri, 31 Mar 2023 12:55:00 +000
- Learning Interpretable Logic for Agent-Based Models from Domain
Independent Primitives
Authors: jordiarranz@improbable.io (Rory Greig; Chris Major, Michalina Pacholska, Sebastian Bending Jordi Arranz
Abstract: Rory Greig, Chris Major, Michalina Pacholska, Sebastian Bending and Jordi Arranz: Genetic programming (GP) is a powerful method applicable to Inverse Generative Social Science (IGSS) for learning non-trivial agent logic in agent-based models (ABMs). While previous attempts at using evolutionary algorithms for learning ABM structures have focused on recombining domain-specific primitives, this paper extends prior work by developing techniques to evolve interpretable agent logic from scratch using a highly flexible domain-specific language (DSL) comprised of domain-independent primitives, such as basic mathematical operators. We demonstrate the flexibility of our method by learning symbolic models in two distinct domains: flocking and opinion dynamics, targeting data generated by reference models. Our results show that the evolved solutions closely resemble the reference models in behavior, generalize exceptionally well, and exhibit robustness to noise. Additionally, we provide an in-depth analysis of the generated code and intermediate behaviors, revealing the training process's progression. We explore techniques for further enhancing the interpretability of the resulting code and include a population-level analysis of the diversity for both models. This research demonstrates the potential of GP in IGSS for learning interpretable agent logic in ABMs across various domains.
PubDate: Fri, 31 Mar 2023 12:54:00 +000
- Social Agents' A Systematic Review of Social Identity Formalizations
Authors: gescholz@uos.de (Geeske Scholz; Nanda Wijermans, Rocco Paolillo, Martin Neumann, Torsten Masson, Émile Chappin, Anne Templeton Geo Kocheril
Abstract: Geeske Scholz, Nanda Wijermans, Rocco Paolillo, Martin Neumann, Torsten Masson, Émile Chappin, Anne Templeton and Geo Kocheril: Simulating collective decision-making and behaviour is at the heart of many agent-based models (ABMs). However, the representation of social context and its influence on an agent's behaviour remains challenging. Here, the Social Identity Approach (SIA) from social psychology offers a promising explanation, as it describes how people behave while being part of a group, how groups interact and how these interactions and ingroup norms can change over time. SIA is valuable for diverse application domains while being challenging to formalise. To address this challenge and enable modellers to learn from existing work, we take stock of ABM formalisations of SIA and present a systematic review of SIA in ABMs. Our results show a diversity of application areas and formalisations of (parts of) SIA without any converging practice towards a default formalisation. Models range from simple to (cognitively) rich, with a group of abstract models in the tradition of opinion dynamics employing SIA to specify group-based social influence. We also found some complex cognitive SIA formalisations incorporating contextual behaviour. Looking at the function of SIA in the models, representing collectives, modelling group-based social influence, and unpacking contextual behaviour stood out. Our review was also an inventory of the formalisation challenge attached to using a very promising social-psychological theory in ABMs, revealing a tendency for reference to domain-specific theories to remain vague.
PubDate: Fri, 31 Mar 2023 12:53:00 +000
- Social Identity and Organizational Control: Results of an Agent-Based
Simulation
Authors: friederike.wall@aau.at (Friederike Wall
Abstract: Friederike Wall: It has long been recognised that the identification of organizational members with their organization could mitigate collective action problems. One domain in economics and management addressing these fundamental problems is organizational control, as it focuses on aligning individuals’ behavior with their organization’s objectives. This paper considers organizational identification in interaction with major traits of the organizational context, including the principal means of organizational control. We have used an agent-based simulation based on the framework NK fitness landscapes. In the model, individuals’ organizational identification was endogenous and caused certain effects in the organizations, which then followed on from these effects. The model controlled for different activation mechanisms for individuals’ organizational identity and for different organizational contexts in terms of task complexity and the prevailing coordination mechanism. The results suggest that the activation mechanisms subtly interfere with task complexity and organizational controls. Organizational identification was robustly beneficial across a wide range of task environments when the activation mode corresponds to organizational mission orientation and results controls. Moreover, organizational identification appears particularly relevant when the action controls grant some autonomy to subordinate decision-makers.
PubDate: Fri, 31 Mar 2023 12:52:00 +000
- Dyadic Interaction Shapes Social Identity in the Axelrod Model Using
Empirical Data
Authors: alejandro.dinkelberg@ul.ie (Alejandro Dinkelberg; Pádraig MacCarron, Paul J. Maher, David JP O'Sullivan Michael Quayle
Abstract: Alejandro Dinkelberg, Pádraig MacCarron, Paul J. Maher, David JP O'Sullivan and Michael Quayle: Group dynamics and inter-group relations influence the self-perception. The Social Identity Approach explains the role of multiple identities, derived from categories or group memberships, in social interaction and individual behaviour. In agent-based models, agents interact with their environment to make decisions and take actions. Thus, we examine to what extent the interaction in an agent-based model natively captures core principles of the Social Identity Approach. To do so, we extend the Axelrod model and the agreement-threshold model with explicit aspects of the Social Identity Approach to assess their influence on the simulation outcomes. We study the variants of the Axelrod model by using Monte Carlo simulations and compare the simulation results with longitudinal survey data of opinions. These extensive simulations favour the Axelrod model and the agreement-threshold model. These models fit, without the explicit embedding of features from the Social Identity Approach, the volatility of the opinion-based features better for the given data sets. Our two extensions of the Axelrod model formalise elements of the Social Identity Approach; however, they do not support the fitness of the model to the data. In the simulations, even in the standard Axelrod model, the social identity affects the development of the agents' identity through the homophily principle, and the agents, in turn, shape their own social identity by social influence. We argue that the Axelrod model and the agreement-threshold model implicitly include social identities as emerging properties of evolving opinion-based groups. In addition to that, the attitudinal data captures the hidden group structure in the attitude positions of the participants. In this way, core features of the Social Identity Approach already implicitly play a role in these empirically-driven agent-based models.
PubDate: Fri, 31 Mar 2023 12:51:00 +000
- The Role of Argument Strength and Informational Biases in Polarization and
Bi-Polarization Effects
Authors: carlo.proietti@ilc.cnr.it (Carlo Proietti; Davide Chiarella
Abstract: Carlo Proietti and Davide Chiarella: This work explores, on a simulative basis, the informational causes of polarization and bi-polarization of opinions in groups. Here, the term 'polarization' refers to a uniform change of the opinion of the whole group towards the same direction, whereas 'bi-polarization' indicates a split of two subgroups towards opposite directions. For the purposes of the present inquiry we expand the model of the Argument Communication Theory of Bi-polarization. The latter is an argument-based multi-agent model of opinion dynamics inspired by Persuasive Argument Theory. The original model can account for polarization as an outcome of pure informational influence, and reproduces bi-polarization effects by postulating an additional mechanism of homophilous selection of communication partners. The expanded model adds two dimensions: argument strength and more sophisticated protocols of informational influence (argument communication and opinion update). Adding the first dimension allows to investigate whether and how the presence of stronger or weaker arguments in the discussion influences polarization and bi-polarization dynamics, as suggested by the original framework of Persuasive Arguments Theory. The second feature allows to test whether other mechanisms related to confirmation bias and epistemic vigilance can act as a driving force of bi-polarization. Regarding the first issue, the simulations we perform show that argument strength has a measurable effect. With regard to the second, our results witness that, in absence of homophily, only very strong types of informational bias can lead to bi-polarization.
PubDate: Fri, 31 Mar 2023 12:50:00 +000
- Social Simulation Models as Refuting Machines
Authors: lrizquierdo@ubu.es (Nicolas Mauhe; Luis R. Izquierdo Segismundo S. Izquierdo
Abstract: Nicolas Mauhe, Luis R. Izquierdo and Segismundo S. Izquierdo: This paper discusses a prominent way in which social simulations can contribute -and have contributed- to the advancement of Science, namely, by refuting some of our (wrong) beliefs about how the real world works. More precisely, social simulations can produce counter-examples that reveal something is wrong in a prevailing scientific assumption. In fact, in this paper we argue that this is a role that many well-known social simulation models in the literature have played and, arguably, it may be one of the main reasons why such well-known models became so popular. To test this hypothesis, in this paper we examine several popular models in the Social Simulation literature and indeed we find that all these models are most naturally interpreted as providers of compelling and reproducible (computer-generated) evidence that refuted some assumption or belief in a prevailing theory. By refuting prevailing theories, these models greatly advanced Science and, in some cases, they even opened up a new research field.
PubDate: Fri, 31 Mar 2023 12:49:00 +000
- Corrigendum to 'Arguments as Drivers of Issue Polarisation in Debates
Among Artificial Agents', Journal of Artificial Societies and Social
Simulation, 25 (1) 4, 2022
Authors: f.kopecky@kit.edu (Felix Kopecky
Abstract: Felix Kopecky: This corrigendum refers to 'Arguments as Drivers of Issue Polarisation in Debates Among Artificial Agents', Journal of Artificial Societies and Social Simulation, 25 (1) 4, 2022.
PubDate: Fri, 31 Mar 2023 12:48:00 +000
- Review of: Introduction to Urban Science Evidence and Theory of Cities as
Complex Systems
Authors: bernardo.furtado@ipea.gov.br (Bernardo Alves Furtado
Abstract: Review of: Introduction to Urban Science Evidence and Theory of Cities as Complex Systems by Bettencourt, Luís M. A., reviewed by Bernardo Alves Furtado
PubDate: Fri, 31 Mar 2023 12:47:00 +000
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