Social Simulation Model Research Articles

Application of Social Simulation Learning Model to Improve Students' Speaking and Writing Skills (Study on English Subjects in Class VII SMP N 3 Bengkulu City in the 2023/2024 academic year)

This study aims to describe the writing and speaking abilities of seventh-grade students at SMPN 3 Bengkulu City as well as the effectiveness of applying the social simulation learning model to improve the writing and speaking abilities of seventh-grade students in English subjects at SMPN 3 Bengkulu City. The study used a quasi-experiment and classroom action research design. Twenty males and forty girls, totaling sixty pupils in the seventh grade at SMPN 3 Bengkulu City, served as the subjects of the class action research. All seventh grade pupils of SMPN 3 Bengkulu City made up the population of the experimental study. Class VII.1 (the experimental class) and class VII.2 (the control class) were the study's samples. Thirty students made up the experimental class, which included thirteen males and seventeen girls, and thirty students from the control class, which included sixteen boys and fourteen girls. The study's findings indicate that: using the social simulation learning model at SMPN 3 Bengkulu City in the 2023–2024 academic year has implications for improving students' speaking skills; using the social simulation learning model at SMPN 3 Bengkulu City in the 2023–2024 academic year has implications for improving students' writing skills; and using the social simulation model is effective for improving students' speaking and writing skills at SMPN 3 Bengkulu City in the 2023–2024 academic year because the results of the students' cognitive assessment have significantly increased.

Simulating and Contrasting the Game of Open Access in Diverse Cultural Contexts: A Social Simulation Model

Open Access is a global cause with the aim of allowing unrestricted access to all scientific research output in electronic formats. This paper presents a model for simulating the game of interests behind this cause in order to investigate ways of promoting the practice of open access. The model represents the following actors: Academics, Administrators, Funders, Publishers and Politicians. Five scenarios were developed to represent both realistic and ideal, interesting, situations. The model was developed using the SocLab platform—a formalization of the sociology of organizational action. It is based on previous descriptions of the game and expert knowledge. A structural analysis permits us to examine the properties of the sub-model behind each scenario. The results corroborate certain intuitions about the scenarios representing realistic cases, e.g., they indicate that publishers, being isolated in their interests, are subject to strong pressures from other actors, who have a circumstantial alliance. Administrators take an intermediate stance in all scenarios. The best scenarios for open access are those in which Politicians and Funders clearly support the cause by expressing mandates in that direction, backing academics. Surprisingly, the model shows that it is in the Publishers’ interest not to take an extremist position against open access.

Impact on Cooperation of Altruism, Tenacity, and Need of a Resource

This article analyzes, through a computer social simulation model (i.e., the SocLab framework), the complex interactions between altruism, tenacity, the need or the importance of a resource, in particular a common resource (stake), and the number of actors and their impacts on cooperation within a system of organized action. It contributes to better understand the tradeoff among these factors for strategic behavior. A free rider model is used, generating results that are general, applicable in diverse situations. Results indicate the following order of importance of the factors: altruism, stakes, tenacity, and number of actors, together with a strong interdependence between altruism and stakes. This article develops a methodology useful to improve the understanding of complex environmental and social problems introduced by the management of common resources by using a well-grounded theoretical and computational framework.

A Survey on Multi Agent System and Its Applications in Power System Engineering

An Intelligent Agent (IA) is a type of autonomous entity in the field of Artificial Intelligence (AI) that gathers information about its surroundings using sensors, takes action in response to that information using actuators ("agent" part), and guides its behavior to achieve predetermined results (i.e. it is rational). Agents that are both intelligent and able to learn or utilize information to accomplish their tasks would be ideal. Similar to how economists study agents, cognitive scientists, ethicists, philosophers of practical reason and researchers in a wide range of other disciplines study variations of the IAmodel used in multidisciplinary socio-cognitive modelling and computer social simulation models. In this article, the term "Multi-Agent System" (MAS) has been used to refer to a system in which two or more autonomous entities communicate with one another. The key objective of this research is to provide a critical analysis of MAS and its applications in power systems. A case study to define the application of MAS in power system is also provided, using a critical implementation of fuzzy logic controllers.

Social Simulation Models as Refuting Machines

Social Simulation Models as Refuting Machines

Realization of Large-Scale Social Simulation Models by an Agent-Based Approach

Realization of Large-Scale Social Simulation Models by an Agent-Based Approach

Social Simulation Model of the Spread and Prevention of the Omicron SARS-CoV-2 Variant

The enhanced virulence and infectiousness of the Omicron variant of SARS-CoV-2 is having more significant impacts on certain socioeconomic areas, and rapidly suppressing the spread of the epidemic remains a priority for maintaining public health security throughout the world. Thus, we applied multi-agent modeling theory to create a social simulation model of Omicron variant transmission and prevention and control in order to analyze the virus transmission status in complex urban systems and its changing trends under different interventions. By considering the six municipal districts under the jurisdiction of Taiyuan City as examples, we developed state transition rules between five types of resident agents, mobility and contact behavior rules, and rules for patient admission behavior by hospital agents. We then conducted multi-scenario simulation experiments based on single measures of pharmacological and non-pharmacological interventions under non-governmental control as well as multiple interventions in combination to evaluate the effects of different measures on rapidly suppressing the spread of the epidemic. The experimental results demonstrated the utility of the model and the multi-agent modeling method effectively analyzed the transmission trends for the Omicron variant, thereby allowing a comprehensive diagnosis of the future urban epidemic situation and providing an important scientific basis for exploring more accurate normalized prevention and control measures.

Staring at the Abyss: a neurocognitive grounded agent-based model of collective-risk social dilemma under the threat of environmental disaster

Increasingly visible climate change consequences challenge carbon-based economies worldwide. While expert knowledge on climate change percolates through political initiatives and public awareness, its translation into large-scale policy actions appears limited. Climate change consequences unequally target regions, countries and social classes, a vital issue for social cooperation. When facing an imminent ecological collapse, in which conditions can self-interested agents gain environmental awareness and settle on a sustainable path of actions when their knowledge of the imminent collapse is bounded? This cooperation emerges from the interaction between individuals and the interaction of various cognitive processes within individuals. This article develops an agent-based model for this emergence of cooperation enriched with the Agent Zero neurocognitive grounded cognitive architecture. We investigate when agents endowed with deliberative, affective and social modules can settle on actions that safeguard their environment through numerical simulations. Our results show that cooperation on sustainable actions is the strongest when the system is at the edge of collapse. Policy measures that increase the environment’s resilience become internalized by the agents and undermine awareness of the ecological catastrophe. Depending on the cognitive channels activated, agent behaviors and reactions to specific interventions significantly vary. Our analysis suggests that taking different cognitive channels, deliberative, affective, social, and others into account, significantly impact results. The complexity of agent cognition deserves more attention to assess parameter sensitivity in social simulation models.

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.

Social identity theory applied to the game of self-regulation of social exchanges based on multiagent systems

ABSTRACT Autonomous intelligent agents participate in complex social situations. It is necessary that agents are aware of their society and still understand their context of action and interaction. Such social situation with other agents is influenced by society’s characteristics, including their norms, values, common interests and objectives, members, and social structures. Each complex society is broken down into social groups, each one with an identity. Social identity theory is seen as an analysis of intergroup relations in social categories based on the cognitive definition and the autoconcept of a social group and their belonging. Researchers have been studying this theory in computing, more specifically in multiagent systems. Social exchanges are the object of study in diverse contexts in which social relations are understood as social exchanges. One of the investigated themes is the self-regulation of social exchanges. The game of self-regulation of social exchanges explores its self-regulation but it does not analyse the agents’ identity. Therefore, this research investigates the integration of psychosocial analysis of social identity theory into the social adaptive complex society of the game of self-regulation of social exchanges. The principal result of this work is the GSRSEPSIT, a social simulation model.

Scientific Construction of Utopia: Social Modeling and Simulation of Migration to No Man's Land

In this paper, we presents a social simulation model for planning migration to no man's land. The model maximize both the economic output and job satisfaction for the workforce with sustainability in the scope. We build a self-adaptive system and define it...

Virtual reality crowd simulation: effects of agent density on user experience and behaviour

Agent-based crowd simulations are used for modelling building and space usage, allowing designers to explore hypothetical real-world scenarios, including extraordinary events such as evacuations. Existing work which engages virtual reality (VR) as a platform for crowd simulations has been primarily focussed on the validation of simulation models through observation; the use of interactions such as gaze to enhance a sense of immersion; or studies of proxemics. In this work, we extend previous studies of proxemics and examine the effects of varying crowd density on user experience and behaviour. We have created a simulation in which participants walk freely and perform a routine manual task, whilst interacting with agents controlled by a typical social force simulation model. We examine and report the effects of crowd density on both affective state and behaviour. Our results show a significant increase in negative affect with density, measured using a self-report scale. We further show significant differences in some aspects of user behaviours, using video analysis, and discuss how our results relate to VR simulation design for mixed human–agent scenarios.

A method of building simulation model for organizational decision-making and inter-organizational control

This paper presented a method on the configuration of a social simulation model intended for general-purpose application as “inter-system control model”, for analyzing real societies engaged in complex and diverse organizational decision-making. The model in this paper is a micro-agent-based model, configured using one replicator dynamics for each decision-making behavior, whether the agent is an organization or an individual. Through the model in this paper, it is possible to configure broad types of decision-making model by individuals, internal organization of corporations, inter-corporate relations and the national economy. This paper describes a method of social simulation model primarily focusing on an inter-organizational decision-making and demonstrates a configuration method for this simulation model using case studies on long-term economic growth model with technological change.

Social Simulation Models at the Ethical Crossroads.

Computational models of group opinion dynamics are one of the most active fields of sociophysics. In recent years, advances in model complexity and, in particular, the possibility to connect these models with detailed data describing individual behaviors, preferences and activities, have opened the way for the simulations to describe quantitatively selected, real world social systems. The simulations could be then used to study 'what-if' scenarios for opinion change campaigns, political, ideological or commercial. The possibility of the practical application of the attitude change models necessitates that the research community working in the field should consider more seriously the moral aspects of their efforts, in particular the potential for their use for unintended goals. The paper discusses these issues, and offers a suggestion for a new research direction: using the attitude models to increase the awareness and detection of social manipulation cases. Such research would offer a scientific challenge and meet the ethical criteria.

An agent-based model for diffusion of electric vehicles

The transition from fuel cars to electric cars is a large-scale process involving many interactions between consumers and other stakeholders over decades. To explore how policies may interact with consumer behavior over such a long time period, we developed an agent-based social simulation model. In this model, detailed data of 1795 respondents have been used to parameterise an agent architecture that addresses different consumer needs and decision strategies. Numerical experiments indicate that effective policy requires a long-lasting implementation of a combination of monetary, structural and informational measures. The strongest effect on emission reduction requires an exclusive support for full battery electric cars and no support for hybrid cars.

MENINGKATKAN SIKAP DEMOKRATIS SISWA DENGAN MODEL PEMBELAJARAN SIMULASI SOSIAL PADA PELAJARAN PKn KONTEKS IPS

This paper is intended to strengthen social studies education especially in citizenship education in developing and increasing students’ democratic attitude through social simulation model. This research is done to expand the students’ awareness of social issues in order to they can play important role as good citizen. Social simulation model is offered as alternative solution to solve the social problem in students’ daily activities in society. The dispute between students group is considered caused by low of students’ awareness of democratic attitude. By developing this model, students can work cooperatively in building social relationship and correspondence, also avoiding solving social problem by anarchies.Key words: citizenship education, students’ democratic attitude, social simulation model.

Modelling domestic water demand: An agent based approach

The urban water system is a complex adaptive system consisting of technical, environmental and social components which interact with each other through time. As such, its investigation requires tools able to model the complete socio-technical system, complementing “infrastructure-centred” approaches. This paper presents a methodology for integrating two modelling tools, a social simulation model and an urban water management tool. An agent based model, the Urban Water Agents' Behaviour, is developed to simulate the domestic water users’ behaviour in response to water demand management measures and is then coupled to the Urban Water Optioneering Tool to calculate the evolution of domestic water demand by simulating the use of water appliances. The proposed methodology is tested using, as a case study, a major period of drought in Athens, Greece. Results suggest that the coupling of the two models provides new functionality for water demand management scenarios assessment by water regulators and companies.

Identifying Salient Drivers of Livelihood Decision-Making in the Forest Communities of Cameroon: Adding Value to Social Simulation Models

This paper describes a participatory and collaborative process for formalising qualitative data, using research from southeast Cameroon, how these results can provide input to an social simulation model, and what insights they can provide in better understanding decision-making in the region. Knowledge Elicitation Tools (KnETs) have been used to support a body of existing research on local strategies that build community adaptive capacity and support sustainable forest management under a range of socio-environmental and climatic stressors. The output of this approach is a set of decision rules which complements previous analysis of differentiated vulnerability of forest communities. Improvements to the KnETs methodology, such as new statistical measurements, make it easier to generate inputs for a social simulation model, such as agent attributes and heterogeneity, as well as informing which scenarios to prioritise during model development and testing. The KnETs process served as a vehicle to structure a large volume of empirical data, to identify the most salient drivers of decision-making amongst different actors, to uncover tacit knowledge and to make recommendations about which strategic interventions should be further explored in a social simulation and by local organizations planning interventions. It was notable that there were many common rule drivers for men and women from the same households, though they participated in the game-interviews separately. At the same time, though strategies were common to both poor and better-off farmers, differences lay in the package of strategies chosen - the number and type of strategies as well the drivers factors - and how they were prioritised with respect to each farmer's goal.

An ontologic agent-based model of recreational polydrug use: SimUse

SimUse is an ontology-based social simulation model aiming at reproducing trajectories of recreational poly-drug users. To describe and capture the complexity of this phenomenon, we bring together empirical evidence from ethnography with theoretical constructs from sociology and neuroscience into an agent-based model. After reviewing the context of recreational poly-substance use and justifying our approach, this paper describes the multi-layered structure of the simulation and details some of the key aspects of SimUse. We illustrate the capacity of SimUse to reproduce neurophysiological reactions to substance use and to explore 'what-if?' scenarios related to drug use.

Artificial neural networks and aggregate consumption patterns in New Zealand

This study engineers a household sector where process macroeconomic information to reproduce consumption spending patterns in New Zealand. To do this, heterogeneous artificial neural networks (ANNs) are trained to forecast changes in per worker consumption. In contrast to existing literature, results suggest that there exists a trained ANN that significantly outperforms a linear econometric model at out-of-sample forecasting. To improve the accuracy of ANNs using only in-sample information, methods for combining private knowledge into social knowledge are explored. For one type of ANN, relying on an expert is beneficial. For most ANN structures, weighting an individuals forecast according to how frequently that individuals ANN is a top performer during in-sample training produces more accurate social forecasts. By focusing only on recent periods, considering the severity of an individuals errors in weighting their forecast is also beneficial. Possible avenues for incorporating ANN structures into artificial social simulation models of consumption are discussed.