scholarly journals Advances in the agent-based modeling of economic and social behavior

2021 ◽  
Vol 1 (7) ◽  
Author(s):  
Mitja Steinbacher ◽  
Matthias Raddant ◽  
Fariba Karimi ◽  
Eva Camacho Cuena ◽  
Simone Alfarano ◽  
...  
Keyword(s):  
Social Behavior ◽  
Laboratory Experiments ◽  
State Of The Art ◽  
Social Systems ◽  
Agent Based Modeling ◽  
Data Sources ◽  
Future Research ◽  
Economic Networks ◽  
Agent Based ◽  
Agent Behavior

AbstractIn this review we discuss advances in the agent-based modeling of economic and social systems. We show the state of the art of the heuristic design of agents and how behavioral economics and laboratory experiments have improved the modeling of agent behavior. We further discuss how economic networks and social systems can be modeled and we discuss novel methodology and data sources. Lastly, we present an overview of estimation techniques to calibrate and validate agent-based models and show avenues for future research.

scholarly journals The Contribution of Social Simulation in the Advancement of Marketing Issues and Challenges

2013 ◽  
Vol 5 (1) ◽  
pp. 19-31
Author(s):  
Mario Gonzalez-Fuentes
Keyword(s):  
State Of The Art ◽  
Holistic Approach ◽  
Agent Based Modeling ◽  
Marketing Strategies ◽  
Social Simulation ◽  
Future Research ◽  
Agent Based ◽  
Research Opportunities ◽  
Computer Scientists ◽  
The One

For some years now, marketers have been praising for a more holistic approach of a company’s marketing efforts across all areas. However, traditional models show serious limitations to address the complexities of managing all of a company’s touch points with a customer. Agent-based modeling (ABM) has opened the door to explore the unfolding behaviors and outputs of an increasingly connected and interactive marketplace. The contribution of this paper is twofold. On the one hand, it provides researchers with a state-of-the-art repository for this strand of research. This facilitates the identification of relevant gaps in the literature and future research avenues. Second, it contributes to assess the way ABM has improved our understanding of the dynamics of markets and its participants when marketing strategies are implemented. Both goals aim at showing the various ways that social simulation has expanded our understanding of marketing and the future research opportunities for both, marketing and computer scientists.

scholarly journals Discussing the Use of Complexity Theory in Engineering Management: Implications for Sustainability

2020 ◽  
Vol 12 (24) ◽  
pp. 10629
Author(s):  
Gianpaolo Abatecola ◽  
Alberto Surace
Keyword(s):  
New Product Development ◽  
Complexity Theory ◽  
Conceptual Modeling ◽  
New Product ◽  
Engineering Management ◽  
Agent Based Modeling ◽  
Current Status ◽  
Future Research ◽  
Agent Based ◽  
Wide Range

What is the state-of-the-art literature regarding the adoption of the complexity theory (CT) in engineering management (EM)? What implications can be derived for future research and practices concerning sustainability issues? In this conceptual article, we critically discuss the current status of complexity research in EM. In this regard, we use IEEE Transactions on Engineering Management, because it is currently considered the leading journal in EM, and is as a reliable, heuristic proxy. From this journal, we analyze 38 representative publications on the topic published since 2000, and extrapolated through a rigorous keyword-based article search. In particular, we show that: (1) the adoption of CT has been associated with a wide range of key themes in EM, such as new product development, supply chain, and project management. (2) The adoption of CT has been witnessed in an increasing amount of publications, with a focus on conceptual modeling based on fuzzy logics, stochastic, or agent-based modeling prevailing. (3) Many key features of CT seem to be quite clearly observable in our dataset, with modeling and optimizing decision making, under uncertainty, as the dominant theme. However, only a limited number of studies appear to formally adhere to CT, to explain the different EM issues investigated. Thus, we derive various implications for EM research (concerning the research in and practice on sustainability issues).

Book Reviews

2014 ◽  
Vol 52 (3) ◽  
pp. 855-858 ◽  
Keyword(s):  
Nuclear Physics ◽  
Matrix Theory ◽  
Agent Based Modeling ◽  
Urn Model ◽  
Economic Networks ◽  
Random Field Ising Model ◽  
Agent Based ◽  
Cross Correlations ◽  
Credit Network

Sixteen papers, based on the conference on “Econophysics of Agent-Based Models” held at the Saha Institute of Nuclear Physics in November 2012, explore agent-based modeling in the field of econophysics from the perspectives of physicists, economists, mathematicians, and financial engineers. Papers discuss agent-based modeling of zapping behavior of viewers, television commercial allocation, and advertisement markets; agent-based modeling of the housing asset bubble—a simple utility function-based investigation; Urn model-based adaptive multi-arm clinical trials—a stochastic approximation approach; logistic modeling of a religious sect cult and financial features; characterizing financial crisis by means of the three states random field Ising model; themes and applications of kinetic exchange models—redux; the kinetic exchange opinion model—solution in the single parameter map limit; an overview of the new frontiers of economic complexity; Jan Tinbergen's legacy for economic networks—from the gravity model to quantum statistics; a macroscopic order of consumer demand due to heterogeneous consumer behavior on Japanese household demand tested by the random matrix theory; uncovering the network structure of the world currency market—cross-correlations in the fluctuations of daily exchange rates; systemic risk in the Japanese credit network; pricing of goods with bandwagon properties—the curse of coordination; evolution of econophysics; econophysics and sociophysics—problems and prospects; and a discussion on econophysics. Abergel and Chakraborti are with the Laboratory of Mathematics Applied to Systems at the École Centrale Paris. Aoyama is with the Department of Physics at Kyoto University. Chakrabarti is at the Saha Institute of Nuclear Physics. Ghosh is with the Theoretical Condensed Matter Physics Division at the Saha Institute of Nuclear Physics.

Agent based modeling of the coevolution of hostility and pacifism

2015 ◽  
Vol 26 (09) ◽  
pp. 1550098 ◽  
Author(s):  
Fermin Dalmagro ◽  
Juan Jimenez
Keyword(s):  
Social Systems ◽  
Stable State ◽  
Agent Based Modeling ◽  
Armed Conflicts ◽  
Agent Based ◽  
Public Data ◽  
Prisoners Dilemma ◽  
Quantitative Characteristics ◽  
Global Peace ◽  
Emergent Behaviors

We propose a model based on a population of agents whose states represent either hostile or peaceful behavior. Randomly selected pairs of agents interact according to a variation of the Prisoners Dilemma game, and the probabilities that the agents behave aggressively or not are constantly updated by the model so that the agents that remain in the game are those with the highest fitness. We show that the population of agents oscillate between generalized conflict and global peace, without either reaching a stable state. We then use this model to explain some of the emergent behaviors in collective conflicts, by comparing the simulated results with empirical data obtained from social systems. In particular, using public data reports we show how the model precisely reproduces interesting quantitative characteristics of diverse types of armed conflicts, public protests, riots and strikes.

Multi-Agent Simulation in Organizations

2011 ◽  
pp. 2728-2733
Author(s):  
Nikola Vlahovic ◽  
Vlatko Ceric
Keyword(s):  
Social Systems ◽  
Real Life ◽  
Agent Based Modeling ◽  
Multi Agent Systems ◽  
Social Constructs ◽  
Social Phenomena ◽  
Agent Based ◽  
Agent Systems ◽  
Agent Simulation ◽  
Multi Agent

Most economic and business systems are complex, dynamic, and nondeterministic systems. Different modeling techniques have been used for representing real life economic and business organizations either on a macro level (such as national economics) or micro level (such as business processes within a firm or strategies within an industry). Even though general computer simulation was used for modeling various systems (Zeigler, 1976) since the 1970s the limitation of computer resources did not allow for in-depth simulation of dynamic social phenomena. The dynamics of social systems and impact of the behavior of individual entities in social constructs were modeled using mathematical modeling or system dynamics. With the growing interest in multi agent systems that led to its standardization in the 1990s, multi agent systems were proposed for the use of modeling social systems (Gilbert & Conte, 1995). Multi agent simulation was able to provide a high level disintegration of the models and proper treatment of inhomogeneity and individualism of the agents, thus allowing for simulation of cooperation and competition. A number of simulation models were developed in the research of biological and ecological systems, such as models for testing the behavior and communication between social insects (bees and ants). Artificial systems for testing hypothesis about social order and norms, as well as ancient societies (Kohler, Gumerman, & Reynolds, 2005) were also simulated. Since then, agent-based modeling and simulation (ABMS) established itself as an attractive modeling technique (Klugl, 2001; Moss & Davidsson, 2001). Numerous software toolkits were released, such as Swarm, Repast, MASON and SeSAm. These toolkits make agent-based modeling easy enough to be attractive to practitioners from a variety of subject areas dealing with social interactions. They make agent-based modeling accessible to a large number of analysts with less programming experience.

On the contributions and the validation of an agent-based simulation model of innovation diffusion

2016 ◽  
Vol 50 (3/4) ◽  
pp. 647-657 ◽  
Author(s):  
Mohammad G. Nejad
Keyword(s):  
Simulation Modeling ◽  
Agent Based Modeling ◽  
Test Theory ◽  
Future Research ◽  
Agent Based Simulation ◽  
Content Type ◽  
Agent Based ◽  
Points Of View ◽  
Complex Phenomena ◽  
Specific Paper

Purpose This paper provides an overview of agent-based modeling and simulation (ABMS) and evaluates the questions that have been raised regarding the “assumptions and mechanisms used” by a well-cited paper that has used this methodology. Design/methodology/approach This work provides a review of agent-based simulation modeling and its capabilities to advance and test theory. The commentary then evaluates and addresses the raised questions and reservations. Findings Agent-based modeling offers unique capabilities that can be used to explore complex phenomena in business and marketing. Some of the raised reservations may be considered as directions for future research. However, the criticisms are for most part unsupported by existing research and do not undermine the contributions of the paper that is being discussed. Practical implications Given its relative novelty, reservations regarding agent-based simulation modeling are quite natural. Discussions like this one would bring together different points of view and lead to a better understanding of how using ABMS can benefit academia and industry. Originality/value This commentary is part of an intellectual dialogue that seeks to provide different points of view about agent-based simulation modeling using a specific paper as an example.

scholarly journals Agent-Based Modeling for Integrating Human Behavior into the Food–Energy–Water Nexus

Land ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 519
Author(s):  
Nicholas R. Magliocca
Keyword(s):  
Decision Making ◽  
Human Behavior ◽  
Significant Proportion ◽  
Agent Based Modeling ◽  
Future Research ◽  
Learning Technology ◽  
Coupled Modeling ◽  
Agent Based ◽  
Food Energy ◽  
Data Limitations

The nexus of food, energy, and water systems (FEWS) has become a salient research topic, as well as a pressing societal and policy challenge. Computational modeling is a key tool in addressing these challenges, and FEWS modeling as a subfield is now established. However, social dimensions of FEWS nexus issues, such as individual or social learning, technology adoption decisions, and adaptive behaviors, remain relatively underdeveloped in FEWS modeling and research. Agent-based models (ABMs) have received increasing usage recently in efforts to better represent and integrate human behavior into FEWS research. A systematic review identified 29 articles in which at least two food, energy, or water sectors were explicitly considered with an ABM and/or ABM-coupled modeling approach. Agent decision-making and behavior ranged from reactive to active, motivated by primarily economic objectives to multi-criteria in nature, and implemented with individual-based to highly aggregated entities. However, a significant proportion of models did not contain agent interactions, or did not base agent decision-making on existing behavioral theories. Model design choices imposed by data limitations, structural requirements for coupling with other simulation models, or spatial and/or temporal scales of application resulted in agent representations lacking explicit decision-making processes or social interactions. In contrast, several methodological innovations were also noted, which were catalyzed by the challenges associated with developing multi-scale, cross-sector models. Several avenues for future research with ABMs in FEWS research are suggested based on these findings. The reviewed ABM applications represent progress, yet many opportunities for more behaviorally rich agent-based modeling in the FEWS context remain.

Agent-Based Modeling and Simulation on Multi-Stage Supply Chain Operation

2011 ◽  
Vol 291-294 ◽  
pp. 3216-3220 ◽  
Author(s):  
Can Can Zhao ◽  
Xiao Dong Zhang ◽  
Shao Juan Lei ◽  
Jun Jiang Qiu
Keyword(s):  
Supply Chain ◽  
Modeling And Simulation ◽  
Cooperative Behavior ◽  
Agent Based Modeling ◽  
Individual Agent ◽  
Agent Based ◽  
Agent Behavior ◽  
Chain Operation ◽  
Multi Stage ◽  
Supply Chain Operation

Supply chain simulation is a fundamental approach for supply chain prediction, management, evaluation, and improvement. In order to simulate member behavior, organizational strategy, and management strategy of the supply chain, an agent-based modeling and simulation approach on multi-stage supply chain operation was proposed in this paper. First, the model structure of multi-stage supply chain operation was introduced. Then, the individual agent behavior model was emphatically studied, and hierarchical colored Petri-nets were used to describe the agent behavior process and cooperative behavior process. Finally, the model was validated using a series of simulation which focused on the agent inventory strategy.

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