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Visualization of Thomas Schelling's (1971) segregation model at its commencement (top panel) and conclusion (bottom panels). When agents have a 15% threshold for similarity (left panel), only minimal segregation occurs. However, 30% (middle panel) and 75% (right panel) thresholds produce striking segregation. Figure retrieved from http://nifty.stanford.edu/2014/mccown-schelling-model-segregation/.
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Agent-based modeling is a longstanding but under-used method that allows researchers to simulate artificial worlds for hypothesis testing and theory building. Agent-based models (ABMs) offer unprecedented control and statistical power by allowing researchers to precisely specify the behavior of any number of agents and observe their interactions ov...
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... Following Meehl's (1978Meehl's ( , 1990 recommendation for more falsifiable theories, recently, there have been widespread calls for the development and testing of more formalized theories and models in psychology (e.g., Borsboom et al., 2021;Fried, 2020;Guest & Martin, 2021;Haslbeck et al., 2022;Jackson et al., 2017;Oberauer & Lewandowsky, 2019;Robinaugh et al., 2021;Scheel et al., 2021;Smaldino, 2017Smaldino, , 2020van Rooij & Baggio, 2021;Wilson & Collins, 2019). In the following section, we discuss the importance of models for the future of our field. ...
... Formal models help address issues of transparency, understanding, rigor, prediction, and application in psychology (for tutorials, examples, and calls for formalization, see Epstein, 2008;Farrell & Lewandowsky, 2010;Ford, 2009;Guest & Martin, 2021;Jackson et al., 2017;Marewski & Olsson, 2009;Smaldino, 2017Smaldino, , 2020Smith & Conrey, 2007;van Rooij & Blokpoel, 2020;Weinhardt & Vancouver, 2012;Wilson & Collins, 2019). In contrast to theories and "verbal models," formal models achieve transparency by assuming a collection of exact mathematical relationships or algorithmic processes (e.g., Fum et al., 2007). ...
Numerous scholars believe that there is a crisis in psychology because of the “poor quality” of our theories. However, we believe that it is misleading to suggest that psychology is going through a “theory crisis” because the major shortcomings of theories in the field have been recognized for decades. More fundamentally, there is nothing temporary about the current state theory in the field. Theories in psychology and other social and behavioral sciences will always fall short of traditional scientific benchmarks because of the complexity of the topics that are studied and the problem of generality. In our view, the most recent recommendations for improving theory in psychology are limited in feasibleness. Following many scholars, we suggest that psychology should turn more to formal modeling to increase rigor and improve prediction. However, while models are potentially of great value to the field, they are not theories. Researchers need to accept the limitations inherent to the study of the complexity of social and behavioral phenomena and stop the unhelpful criticism of our field. They also need to recognize the cumulativeness of psychological theory and the enormous body of knowledge of psychological processes, structures, and effects that have been generated by research. Although theories in our field are often sketchy, they are indispensable in providing explanations for important phenomena, suggesting interventions and treatments for critical social and behavioral problems and facilitating the development of predictive models.
... In the modelling, the agents represent human individuals, interact with each other and with the system environment explicitly and individually. Compared to the other tools, such as laboratory or filed experiments, ABMs apply the experimental control on large scale population to capture the nonlinear societal mechanisms and reveal largescale societal emergence (Jackson et al. (2017)). ...
Understanding the dynamics of public opinion evolution on online social platforms is critical for analyzing influence mechanisms. Traditional approaches to influencer analysis are typically divided into qualitative assessments of personal attributes and quantitative evaluations of influence power. In this study, we introduce a novel simulated environment that combines Agent-Based Modeling (ABM) with Large Language Models (LLMs), enabling agents to generate posts, form opinions, and update follower networks. This simulation allows for more detailed observations of how opinion leaders emerge. Additionally, we present an innovative application of Reinforcement Learning (RL) to replicate the process of opinion leader formation. Our findings reveal that limiting the action space and incorporating self-observation are key factors for achieving stable opinion leader generation. The learning curves demonstrate the model's capacity to identify optimal strategies and adapt to complex, unpredictable dynamics.
... The main obstacle to answering these questions is that transformative societal change is rare and investigating it empirically in relation to individual transformations is highly challenging. Therefore, it is likely that Step 7 will to a large degree rely on computer simulations such as agent-based modelling (Jackson et al., 2017) rather than on empirical investigations. For example, agent-based modelling is a computational method that simulates interactions of autonomous agents (e.g., individual human beings, organisations) to assess their effects on the system (e.g., society) as a whole (Smith and Conrey, 2007;Jackson et al., 2017;Folke and Kennedy, 2021). ...
... Therefore, it is likely that Step 7 will to a large degree rely on computer simulations such as agent-based modelling (Jackson et al., 2017) rather than on empirical investigations. For example, agent-based modelling is a computational method that simulates interactions of autonomous agents (e.g., individual human beings, organisations) to assess their effects on the system (e.g., society) as a whole (Smith and Conrey, 2007;Jackson et al., 2017;Folke and Kennedy, 2021). The researcher determines the characteristics of these agents and the rules based on which they interact to examine how the system comprising the agents changes over time and what kind of trends emerge. ...
Since the emergence of psychological and behavioural science, one of its foundational goals has been to explain human behaviour. Although the discipline has been highly successful in this endeavour, there is an elephant in the room. Psychological and behavioural science has neglected studying the most challenging aspect of human behaviour−transformative behavioural change. This change can be described as a fundamental and difficult-to-achieve shift in someone's actions that involves a transformation of one's way of living. Understanding transformative behavioural change is essential not only for psychological and behavioural science to accomplish its foundational goal but also to maintain its contemporary relevance. Indeed, it is imminent that both solving the world's biggest issues (e.g., climate change) and living through major disruptions (e.g., technological revolution) will require people to transform their behaviour. In this perspective, I first review and discuss previous relevant research, and then propose a seven-step agenda for how psychological and behavioural science can become the science of transformative behavioural change.
... ABM, especially suitable for the social sciences (Gilbert & Terna, 2000;Jackson et al., 2017), is the subject of a growing number of studies , as, according to Smith et al. (Smith & Conrey, 2007), the ABM approach fits well with the theoretical concerns of social psychology. In the social sciences, an agent is usually assumed to represent an individual person. ...
The successful adoption of social innovations, such as renewable energy systems or pollution reduction plans in cities, depends, to a large extent, on the willingness and participation of the population in their development and implementation. We present an agent‐based model (ABM) to analyze the process of citizen acceptability of a social innovation that uses a variety of agents to represent individual citizens and relevant groups of citizens. Citizen agents make use of the HUMAT cognitive decision‐making model, based on psychosocial theories, to decide on their support for the social innovation considering how their needs will be satisfied if they decide to support (or not) the innovation project, and the influence exerted by the agents in their environment. The ABM was initially developed to represent the urban and transport planning superblock project in the city of Vitoria‐Gasteiz (Spain). The ABM simulations make it possible to study the evolution of public acceptance of social innovation, with the results providing insights to the social dynamics and individual factors that affect the acceptance of the project, enabling an evaluation of how to devise new policies that increase public acceptance. Sufficiently generic to be easily adaptable to different types of social innovations, the ABM is a powerful tool to explore different scenarios and design strategies that foster the acceptance and sustainable adoption of social innovations.
... Bringing together these methodological and theoretical advancements, we developed a theoretically informed, empirically evidenced agent-based model (DIMESim) to simulate effects of interactions between protesters, authorities, and supporters over time and at scale. As we show below, incorporating these features into a computational model allows for the emergence of complex collective phenomena in ways that would elude traditional research methodologies (interviews, surveys, experiments) but provide powerful tests of propositions about the conditions for an enduring movement and emergent radicalism [53]. ...
We are living in an age of protest. Although we have an excellent understanding of the factors that predict participation in protest, we understand little about the conditions that foster a sustained (versus transient) movement. How do interactions between supporters and authorities combine to influence whether and how people engage (i.e., using conventional or radical tactics)? This paper introduces a novel, theoretically-founded and empirically-informed agent-based model (DIMESim) to address these questions. We model the complex interactions between the psychological attributes of the protester (agents), the authority to whom the protests are targeted, and the environment that allows protesters to coordinate with each other -- over time, and at a population scale. Where an authority is responsive and failure is contested, a modest sized conventional movement endured. Where authorities repeatedly and incontrovertibly fail the movement, the population disengaged from action but evidenced an ongoing commitment to radicalism (latent radicalism).
... Agent Based Model (ABM) is an AI approach in the form of computational simulation that performs a bottom-up approach by combining irrational rational agent interactions through networks in microenvironments [9,10]. Agents are adaptive in responding to their environment and interact all the time in an adapted environment [10]. ...
... Agent Based Model (ABM) is an AI approach in the form of computational simulation that performs a bottom-up approach by combining irrational rational agent interactions through networks in microenvironments [9,10]. Agents are adaptive in responding to their environment and interact all the time in an adapted environment [10]. The nature of complex agent interactions captures the properties of the real financial system, in particular the emergence of heterogeneity and limited rationality [11]. ...
... The ABM approach is popular among social sciences for analyzing financial markets because it is built from heuristic rules of agent behavior [12,13]. Agents are able to control goals, circumstances, and behavior during the simulation [10]. Agents are adaptive so that through their character in behaving and interacting they can capture a series of behaviors that give rise to phenomena [14,15]. ...
The fundamental theory of the Efficient market hypothesis (EMH), which states that market participants are rational, has received a lot of criticism. The complexity of behavior in the capital market is still a black box, especially when psychological biases influence aggressively on decision-making amid uncertainty. Experimental research on finance and capital markets in the form of AI using machine learning seeks to predict the results of more complex interactions. This multidisciplinary approach offers efforts to explain social phenomena from the micro level to macro descriptions which are built artificially through the computational world. The processing modeling approach is preferred because it includes the complexes that emerge from the behavior and interactions of individuals in the real world. Agent Based Model (ABM) is an AI approach in the form of computational simulation that performs a bottom-up approach by combining irrational–rational agent interactions through networks in microenvironments. Using the ABM approach through Netlogo computing, this study proves that AI can be used to analyze investor behavior in the capital market. Keywords: Agent Based Model, artificial intelligence, investor behavior
... It is also one of the recommended methods for modeling human behavior (Duffy 2021), alongside system dynamics modeling, game theory, Monte Carlo simulation, network analysis, mathematical modeling, and machine learning. It allows us to test various parameters and compare the model data to add or rule out parameters that impact the emerging behavior (Mollona 2008) and to test various questions (Jackson et al. 2017). We also utilized the Monte Carlo Simulation because it involves using random sampling to model the behavior of agents with uncertainty. ...
... ABM is a bottom-up approach for studying emerging patterns from simple interactions among agents (Duffy 2021). It is applied in a plethora of disciplines such as sociology, economics, political science (Jackson et al. 2017), and applications that range from cargo routing to Artificial Intelligence (Duffy 2021). These models can be a great choice for researchers because they allow for a high level of control on the experiment; can be run an infinite number of times (on a large scale) with all reasonable values for parameters that can allow researchers to generate all possible outputs (Mollona 2008); able to model nonlinear dynamics over time; and allow researchers to test questions that otherwise would not be possible because of ethical concerns (Jackson et al. 2017). ...
... It is applied in a plethora of disciplines such as sociology, economics, political science (Jackson et al. 2017), and applications that range from cargo routing to Artificial Intelligence (Duffy 2021). These models can be a great choice for researchers because they allow for a high level of control on the experiment; can be run an infinite number of times (on a large scale) with all reasonable values for parameters that can allow researchers to generate all possible outputs (Mollona 2008); able to model nonlinear dynamics over time; and allow researchers to test questions that otherwise would not be possible because of ethical concerns (Jackson et al. 2017). Being able to test all parameters allows researchers to compare the model data and add or rule out variables that impact the emerging behavior (Mollona 2008). ...
A mob is an event that is organized via social media, email, SMS, or other forms of digital communication technologies in which a group of people (who might have an agenda) get together online or offline to collectively conduct an act and then disperse (quickly or over a long period). In recent years, these events are increasingly happening worldwide due to the anonymity of the internet, affordability of social media, boredom, etc. Studying such a phenomenon is difficult due to a lack of data, theoretical underpinning, and resources. In this research, we use the Agent-Based Modeling (ABM) technique to model the mobbers and the Monte Carlo method to assign random values to the factors extracted from the theory of Collective Action and conduct many simulations. We also leverage our previous research on Deviant Cyber Flash Mobs to implement various scenarios the mobber could face when they decide to act in a mob or not. This resulted in a model that can simulate mobs, estimate the mob success rate, and the needed powerful actors (e.g., mob organizers) for a mob to succeed. We finally evaluate our model using real-world mob data collected from the Meetup social media platform. This research is one step toward fully understanding mob formation and the motivations of its participants and organizers.
... Then, I introduce my hypotheses and test them using agent-based simulation (Macal, 2016). This approach is particularly useful to make sense of macro-level social phenomena through microlevel behavioral detail (Squazzoni et al., 2014) and infer causal relationships between the independent and dependent variables that are otherwise difficult to be manipulated and captured in real settings due to ethical, economical, and practical reasons (Bonabeau, 2002;Jackson et al., 2017). ...
Facebook, the largest social networking site in the world, has overcome the structural barriers that historically constrain individuals to reach out to different others. Through the platform, people from all walks of life and virtually any location can develop diverse friendships online. However, friendships on Facebook have been as segregated as friendships in real life. This research sought to understand why the leading social networking site intended to “bring the world closer together” retains segregated friendship. In doing so, I employed a series of agent-based simulations based on the Framework for Intergroup Relations and Multiple Affiliations Networks (FIRMAN). As demonstrated, Facebook has primarily enhanced users’ capacity to maintain a larger number of friendships (tie capacity), but it has done little to empower users’ ability to accept diversity and befriend different others (tie outreachability). Facebook must focus on the latter should they truly wish to contribute to the development of a more inclusive society. While in this study I focus on ethnically segregated friendship on Facebook, I argue that the same explanation might also hold for racially and ideologically segregated friendships on other bidirectional social networking sites.
... Other approaches that can be integrated with social psychology for the study of opinion dynamics are mathematical modeling [34][35][36] and agent-based simulations [37,38]. Recently, the integration of other approaches, such as agent-based modeling, has become more popular (see, e.g., [39]), because they make it possible to consider several phenomena in social psychology "as emergent results of dynamically interactive processes taking place in their contexts" [40]. This is part of the quiet methodological revolution referred to in [41], as statistics moves from the mechanical application of a series of procedures to the building and the evaluation of models. ...
In this paper, we study the influence of a small group of agents (i.e., a lobby) that is trying to spread a rumor in a population by using the known model proposed by Serge Galam. In particular, lobbies are modeled as subgroups of individuals who strategically choose their seating in the social space in order to protect their opinions and influence others. We consider different social gatherings and simulate, using finite Markovian chains, opinion dynamics by comparing situations with a lobby to those without a lobby. Our results show how the lobby can influence opinion dynamics in terms of the prevailing opinion and the mean time to reach unanimity. The approach that we take overcomes some of the problems that behavioral economics and psychology have recently struggled with in terms of replicability. This approach is related to the methodological revolution that is slowly changing the dominant perspective in psychology.
... Recently, the integration of other approaches such as Agent Based Modeling is becoming more and more popular (see e.g. [29]) because they make possible to consider several phenomena in social psychology "as emergent results of dynamically interactive processes taking place in their contexts" [30]. This is part of the methodological quiet revolution mentioned in [31] as statistics is moving from the mechanical application of a series of procedures to the building and the evaluation of models. ...
In this paper we study the influence of a small group of agents (i.e., a lobby) who is trying to spread a rumor in a population using the well known model suggested by Galam. In particular, lobbies are modeled as subgroups of people who strategically choose their seating in the social space in order to protect their opinion and influence the others. We consider different social gatherings and simulate the opinion dynamics comparing situations in which a lobby is present to those without lobby. Our results show how the lobby can influence the opinion dynamics in terms of the prevailing opinion and the mean time to reach unanimity. The approach we follow overcomes some of the issues that behavioral economics and psychology have recently encountered in terms of replicability. This approach relates to the methodological revolution which is slowly changing the perspective in psychology.