Agent-based Social Simulation Research Articles

Agent-based social simulations for health crises response: utilising the everyday digital health perspective.

There is increasing recognition of the role that artificial intelligence (AI) systems can play in managing health crises. One such approach, which allows for analysing the potential consequences of different policy interventions is agent-based social simulations (ABSS). Here, the actions and interactions of autonomous agents are modelled to generate virtual societies that can serve as a "testbed" for investigating and comparing different interventions and scenarios. This piece focuses on two key challenges of ABSS in collaborative policy interventions during the COVID-19 pandemic. These were defining valuable scenarios to simulate and the availability of appropriate data. This paper posits that drawing on the research on the "everyday" digital health perspective in designing ABSS before or during health crises, can overcome aspects of these challenges. The focus on digital health interventions reflects a rapid shift in the adoption of such technologies during and after the COVID-19 pandemic, and the new challenges this poses for policy makers. It is argued that by accounting for the everyday digital health in modelling, ABSS would be a more powerful tool in future health crisis management.

Part One (Describing Criminality): The Society Agent Simulating (SAS) & Usage of Oral and Dental Diseases to Predict Criminals Earlier

Agent Based Modeling and Simulation & Usage of Oral and Dental Diseases to predict criminals & crimes: in part one (this paper) of our research description we only try to describe the knowledge of Cognitive Science, Agent-Based Simulation, Social Simulation, Artificial Intelligence and Multi Agent Systems. In the other hand in the second part (next paper) we will show how we can use the oral & dental diseases as a most important data for the Agent-Based Social Simulation, to early predict the criminals and crimes. In social simulating to create social behavioral modeling we need to apply the cognitive abilities. The cognitive ability is the most important property for the social agent. But the agent architecture is not included this ability. The agent with the cognitive ability will be able to improve the models of the behaviors in the society. This ability not only will be useful in the area of the society security and immigrant’s behavior in the target society, but also it is very useful in business and management fields. In this paper we are going to indicate different fields that can be used to predict the behavior of human. To know the different behaviors can make us to be able to predict criminals earlier by applying the comparison target’s internal harmony of life with the external harmony of world. So we will be able to predict the criminals before doing the crimes. It will help us to have secure life. Besides it will be useful in any other fields of life such as business, banking, political aspects, human resources and etc.

Part Three (Cure Most Diseases by Logical Nutrition Treatment): The Society Agent Simulating (SAS) & Usage of Oral and Dental Diseases to Predict Criminals Earlier

Agent Based Modeling and Simulation & Usage of Oral and Dental Diseases to cure criminals & crimes without using jail. In this paper of our research description we only try to describe the knowledge of Nutrition Science to cure most of the diseases. In the other hand in the part (next paper) we will show how we can use the oral & dental diseases as a most important data for the Agent-Based Social Simulation, to early predict the diseases. The cognitive ability is the most important property for the social agent. But the agent architecture is not included this ability. In this study after predicting the diseases before any unhealthiness happening we try to give some solutions to cure them by the logical nutrition. To cure them we need more effective ways for the treatment by logical nutrition. We try to introduce the research leading to obtain more benefits during the treatment.

Part Two (Cure the Criminals By Logical Nutrition Treatment): The Society Agent Simulating (SAS) & Usage of Oral and Dental Diseases to Predict Criminals Earlier

Agent Based Modeling and Simulation & Usage of Oral and Dental Diseases to cure criminals & crimes without using jail. In part one (this paper) of our research description we only try to describe the knowledge of Nutrition Science to cure predicted early criminals. In the other hand in the part (next paper) we will show how we can use the oral & dental diseases as a most important data for the Agent-Based Social Simulation, to early predict the criminals and crimes. In social simulating to create social behavioral modeling we need to apply the cognitive abilities. The cognitive ability is the most important property for the social agent. But the agent architecture is not included this ability. In this study after predicting the criminals before any crime happening we try to give some solutions to cure them by the nutrition. To cure them we need more effective ways for the treatment by nutrition. I try to introduce the research leading to obtain more benefits during the treatment.

Deep Uncertainty Analysis for Social Policy Making by Agent-Based Social Simulation

Deep Uncertainty Analysis for Social Policy Making by Agent-Based Social Simulation

Should we make predictions based on social simulations?

ABSTRACT The general feeling is that no predictions can be made based on agent-based social simulations. The outcomes of social simulations are based on the behaviors of individuals and their interactions. Behavioral models are always incomplete and often, also incorrect with respect to real behavior and thus the outcomes of agent-based social simulations cannot be trusted as predictions. In this article, we argue that behavioral models do not have to be valid in all respects, but only in the essential aspects in order to be able to make useful predictions. Based on some case studies on the effectiveness of restrictions during the COVID-19 crisis, we show that what are essential aspects of a behavioral model that need to be valid depends on the specific situation that is simulated. The predictions that were needed for the COVID-19 crisis were made with an agent-based social simulation framework using a behavioral model based on needs. The predictions could indicate the relative increase or decrease of COVID-19 infections due to the introduction of a new restriction. It shows that useful predictions can be made based on social simulations, but that we have to be careful on what type of predictions to make.

What kind of prediction? Evaluating different facets of prediction in agent-based social simulation

ABSTRACT Researchers have become increasingly interested in the potential use of agent-based modelling for the prediction of social phenomena, motivated by the desire, first, to further cement the method’s scientific status and, second, to participate in other scenarios, particularly in the aid of decision-making. This article contributes to the current discussion on prediction from the perspective of the disciplinary organisation of agent-based social simulation. It addresses conceptual and practical challenges pertaining to the community of practitioners, rather than individual instances of modelling. As such, it provides recommendations that invite both collective critical discussion and cooperation. The first two sections review conceptual challenges associated with the concept of prediction and its instantiation in the computational modelling of complex social phenomena. They identify methodological gaps and disagreements that warrant further analysis. The second two sections consider practical challenges related to the lack of a prediction framework that, on one hand, gives meaning and accommodates everyday prediction practices and, on the other hand, establishes more clearly the connection between prediction and other epistemic goals. This coordination at the practical level, it is claimed, might help to better position prediction with agent-based modelling within the larger social science’s methodological landscape.

An Agent-Based Social Simulation for Citizenship Competences and Conflict Resolution Styles

The development of citizenship competences plays an important role in a complex system like society. Thus, to analyze how such competences impact other contexts is a great challenge because this kind of study involves the work with people and the use of variables that depend on human behaviors. In this sense, many studies have highlighted the advantage of using simulation systems and tools. In particular, the agent-based social simulation field relies upon the Semantic Web to manage knowledge representation in social scenarios. This study focuses on how citizenship competences impact conflict resolution. Moreover, a simulation model in which citizens interact to resolve conflicts by considering citizenship competences and conflict resolution styles is also introduced. It was developed in NetLogo together with an extension that connects it with the ontology of competences. Results show that the higher interactions of citizens-conflicts, the higher level of citizenship competences, and the number of conflicts solved is higher when using citizenship competences.

From agent to action: The use of ethnographic social simulation for crime research

This paper proposes a methodology for grounding agent-based social simulation in ethnographic data, using the example of crime research. The application of computational tools in crime research typically entails a removal of the “intelligible frame” of criminal behaviour and, hence, of meaningful evidence. Ethnography is a microscopic research tradition geared towards the preservation of contextualized meaning deemed essential for the exploration of the variety of prospective alternative scenarios and, hence, of plausible futures. On the basis of exemplary empirical material from a qualitative study on the transit trade of cocaine in the Netherlands, this paper looks into the complementarity and potential integration of the research traditions of ethnography and agent-based modelling. That is to say, it explores the compatibility of the formal languages of both these domains and the mutual benefit of “stitching together” these at first sight very different methods. The ethnographic approach to social simulation specifies the what-if relations of traditional/conventional ABM modelling into condition-action sequences. As we contend, it is exactly this more microscopic level of condition-action sequences that is needed to facilitate ”thick description” and, in turn, enable the grounding of ABM in meaningful evidence.

Analysis and Visualization of High-Dimensional Dynamical Systems’ Phase Space Using a Network-Based Approach

The concept of attractors is considered critical in the study of dynamical systems as they represent the set of states that a system gravitates toward. However, it is generally difficult to analyze attractors in complex systems due to multiple reasons including chaos, high-dimensionality, and stochasticity. This paper explores a novel approach to analyzing attractors in complex systems by utilizing networks to represent phase spaces. We accomplish this by discretizing phase space and defining node associations with attractors by finding sink strongly connected components (SSCCs) within these networks. Moreover, the network representation of phase space facilitates the use of well-established techniques of network analysis to study the phase space of a complex system. We show the latter by introducing a new node-based metric called attractivity which can be used in conjunction with the SSCC as they are highly correlated. We demonstrate the proposed method by applying it to several chaotic dynamical systems and a large-scale agent-based social simulation model.

Evaluating the Influence of Twitter Bots via Agent-Based Social Simulation

Social Media is used by many as a source of information for current world events, followed by publicly sharing their sentiment about these events. However, when the shared information is not trustworthy and receives a large number of interactions, it alters the public’s perception of authentic and false information, particularly when the origin of these stories comes from malicious sources. Over the past decade, there has been an influx of users on the Twitter social network, many of them automated bot accounts with the objective of participating in misinformation campaigns that heavily influence user susceptibility to fake information. This can affect public opinion on real-life matters, as previously seen in the 2020 presidential elections and the current COVID-19 epidemic, both plagued with misinformation. In this paper, we propose an agent-based social simulation environment that utilizes the social network Twitter, with the objective of evaluating how the beliefs of agents representing regular Twitter users can be influenced by malicious users scattered throughout Twitter with the sole purpose of spreading misinformation. We applied two scenarios to compare how these regular agents behave in the Twitter network, with and without malicious agents, to study how much influence malicious agents have on the general susceptibility of the regular users. To achieve this, we implemented a belief value system to measure how impressionable an agent is when encountering misinformation and how its behavior gets affected. The results indicated similar outcomes in the two scenarios as the affected belief value changed for these regular agents, exhibiting belief in the misinformation. Although the change in belief value occurred slowly, it had a profound effect when the malicious agents were present, as many more regular agents started believing in misinformation.

The Ethics of Agent-Based Social Simulation

The Ethics of Agent-Based Social Simulation

Implementations, interpretative malleability, value-laden-ness and the moral significance of agent-based social simulations

The focus of social simulation on representing the social world calls for an investigation of whether its implementations are inherently value-laden. In this article, I investigate what kind of thing implementation is in social simulation and consider the extent to which it has moral significance. When the purpose of a computational artefact is simulating human institutions, designers with different value judgements may have rational reasons for developing different implementations. I provide three arguments to show that different implementations amount to taking moral stands via the artefact. First, the meaning of a social simulation is not homogeneous among its users, which indicates that simulations have high interpretive malleability. I place malleability as the condition of simulation to be a metaphorical vehicle for representing the social world, allowing for different value judgements about the institutional world that the artefact is expected to simulate. Second, simulating the social world involves distinguishing between malfunction of the artefact and representation gaps, which reflect the role of meaning in simulating the social world and how meaning may or not remain coherent among the models that constitute a single implementation. Third, social simulations are akin to Kroes’ (Kroes, Technical artefacts: creations of mind and matter: a philosophy of engineering design, Springer, Dordrecht, 2012) techno-symbolic artefacts, in which the artefact’s effectiveness relative to a purpose hinges not only on the functional effectiveness of the artefact’s structure, but also on the artefact’s meaning. Meaning, not just technical function, makes implementations morally appraisable relative to a purpose. I investigate Schelling’s model of ethnic residential segregation as an example, in which different implementations amount to taking different moral stands via the artefact.

The Theory-Practice Gap in the Evaluation of Agent-Based Social Simulations.

Agent-based social simulations have historically been evaluated using two criteria: verification and validation. This article questions the adequacy of this dual evaluation scheme. It claims that the scheme does not conform to everyday practices of evaluation, and has, over time, fostered a theory-practice gap in the assessment of social simulations. This gap originates because the dual evaluation scheme, inherited from computer science and software engineering, on one hand, overemphasizes the technical and formal aspects of the implementation process and, on the other hand, misrepresents the connection between the conceptual and the computational model. The mismatch between evaluation theory and practice, it is suggested, might be overcome if practitioners of agent-based social simulation adopt a single criterion evaluation scheme in which: i) the technical/formal issues of the implementation process are tackled as a matter of debugging or instrument calibration, and ii) the epistemological issues surrounding the connection between conceptual and computational models are addressed as a matter of validation.

Action network: a probabilistic graphical model for social simulation

Agent-based social simulations are typically described in imperative form. While this facilitates implementation as computer programs, it makes implicit the different assumptions made, both about the functional form and the causal ordering involved. As a solution to the problem, a probabilistic graphical model, Action Network (AN), is proposed in this paper for social simulation. Simulation variables are represented by nodes, and causal links by edges. An Action Table is associated with each node, describing incremental probabilistic actions to be performed in response to fuzzy parental states. AN offers a graphical causal model that captures the dynamics of a social process. Details of the formalism are presented along with illustrative examples. Software that implements the formalism is available at http://actionnetwork.epizy.com .

Social Epistemology and Validation in Agent-Based Social Simulation

Social Epistemology and Validation in Agent-Based Social Simulation

Reduced Meat Consumption: from Multicriteria Argument Modelling to Agent-Based Social Simulation

A second nutrition transition seems to be emerging towards more plant-based diets, curbing meat consumption in developed countries at the beginning of the 21st century. This shift suggests that rational arguments tend to influence an increasing number of individuals to adopt vegetarian diets. This work aimed to understand and simulate the impact of different types of messages on the choice to change food diets at the individual level, and the impact of the diffusion of opinions at the collective level. It provided two results: (1) a network of arguments around vegetarian diets is modelled using an abstract argumentation approach. Each argument, formalized by a node, was connected with other arguments by arrows, thus formalizing relationships between arguments. This methodology made it possible to formalize an argument network about vegetarian diets and to identify the importance of health arguments compared to ethical or other types of arguments. This methodology also identified key arguments as a result of their high centrality in being challenged or challenging other arguments. The results of constructing this argument network suggested that any controversy surrounding vegetarian diets will be polarized around such high centrality arguments about health. Even though few ethical arguments appeared in our network, the health arguments concerning the necessity or not of animal products for humans were indirectly connected with ethical choices towards vegetarian diets; (2) an agent-based simulation of the social diffusion of opinions and practices concerning meat consumption is then introduced. The purpose of this simulation was to capture the balance of vegetarian vs. meat-based diets. It contributes to modelling consumer choices by exploring the balance between individual values and external influences such as social pressure, communication campaigns and sanitary, environmental or ethical crises.

Autonomous Agents in 3D Crowd Simulation Through BDI Architecture

Agent based simulation (ABS) is a paradigm to modelling systems included of autonomous and interacting agents. ABS has been tremendous growth and used by researchers in the social sciences to study socio-environmental complex systems. To date, various platforms have been introduced for agent-based social simulation. They are rule based in any logic, python based in SPADE and etc. Although those platforms have been introduced, there is still an insufficient to develop a crowd simulation in 3D platform. Having a 3D platform is needed to enabling the crowd simulation for training purposes. However, the current tools and platform still lack features to develop and simulate autonomous agents in the 3D world. This paper introduced a BDI plug in at Unity3D for crowd simulation. BDI is an intelligent agent architecture and it is able to develop autonomous agents in crowd environment. In this paper, we present the BDI plug with a case study of Australia bush fire and discuss a method to support autonomous agents' development in 3D crowd simulation. The tool allows the modeller to develop autonomous agents in 3D world by taking the advantages of Unity3D.

Agent-Based Social Simulation of the Covid-19 Pandemic: A Systematic Review

When planning interventions to limit the spread of Covid-19, the current state of knowledge about the disease and specific characteristics of the population need to be considered. Simulations can facilitate policy making as they take prevailing circumstances into account. Moreover, they allow for the investigation of the potential effects of different interventions using an artificial population. Agent-based Social Simulation (ABSS) is argued to be particularly useful as it can capture the behavior of and interactions between individuals. We performed a systematic literature reviewand identified 126 articles that describe ABSS of Covid-19 transmission processes. Our reviewshowed that ABSS is widely used for investigating the spread of Covid-19. Existing models are very heterogeneous with respect to their purpose, the number of simulated individuals, and the modeled geographical region, as well as how they model transmission dynamics, disease states, human behavior, and interventions. To this end, a discrepancy can be identified between the needs of policy makers and what is implemented by the simulation models. This also includes how thoroughly the models consider and represent the real world, e.g. in terms of factors that affect the transmission probability or how humans make decisions. Shortcomingswere also identified in the transparency of the presented models, e.g. in terms of documentation or availability, as well as in their validation, which might limit their suitability for supporting decision-making processes. We discuss how these issues can be mitigated to further establish ABSS as a powerful tool for crisis management.

Agent-Based Social Simulation for 120 Million People

Agent-Based Social Simulation for 120 Million People