Journal articles on the topic 'Models of an economic agent'
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Chen, Shu-Heng, Chia-Ling Chang, and Ye-Rong Du. "Agent-based economic models and econometrics." Knowledge Engineering Review 27, no. 2 (April 26, 2012): 187–219. http://dx.doi.org/10.1017/s0269888912000136.
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AbstractThis paper reviews the development of agent-based (computational) economics (ACE) from an econometrics viewpoint. The review comprises three stages, characterizing the past, the present, and the future of this development. The first two stages can be interpreted as an attempt to build the econometric foundation of ACE, and, through that, enrich its empirical content. The second stage may then invoke a reverse reflection on the possible agent-based foundation of econometrics. While ACE modeling has been applied to different branches of economics, the one, and probably the only one, which is able to provide evidence of this three-stage development is finance or financial economics. We will, therefore, focus our review only on the literature of agent-based computational finance, or, more specifically, the agent-based modeling of financial markets.
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Dawid, Herbert, and Michael Neugart. "Agent-based Models for Economic Policy Design." Eastern Economic Journal 37, no. 1 (December 28, 2010): 44–50. http://dx.doi.org/10.1057/eej.2010.43.
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Fagiolo, G., and A. Roventini. "On the Scientific Status of Economic Policy: A Tale of Alternative Paradigms." Voprosy Ekonomiki, no. 6 (June 20, 2009): 24–47. http://dx.doi.org/10.32609/0042-8736-2009-6-24-47.
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The article considers and compares two modern approaches to the scientific foundation of economic policy. The first one is a part of mainstream economics and involves developing dynamic stochastic general equilibrium models for analyzing business cycles and exogenous monetary shocks. The second one (which the authors prefer) is a creation of "agent-based models" with more realistic assumptions on the behavior and interactions of agents, absence of equilibrium and analytical solutions, but with computer simulations. The article criticizes an equilibrium approach, describes some results of application of agent-based computational economics to the analysis of economic policy and mentions some difficulties arising whenever this new theoretical paradigm is applied.
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Richmond, Peter, and Lorenzo Sabatelli. "Langevin processes, agent models and socio-economic systems." Physica A: Statistical Mechanics and its Applications 336, no. 1-2 (May 2004): 27–38. http://dx.doi.org/10.1016/j.physa.2004.01.007.
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Tsyplakov, Alexander A. "Agent-Based Modeling of Spatial Economic Systems: а Review." Journal of Siberian Federal University. Humanities & Social Sciences 14, no. 12 (December 2021): 1910–19. http://dx.doi.org/10.17516/1997-1370-0869.
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The paper describes the current state of agent-based modeling of geographical space and spatial economic systems. We explore reasons why this approach to modeling spatial phenomena is of particular interest. Agent-based models (ABMs) allow accounting for agents’ spatial heterogeneity, the existing structure of the space, locality of interactions between agents. A survey of approaches to introducing space into the models and examples of the existing spatial models is presented. There is a great variety of spatial ABMs, but they relate predominantly to the local and city level, rather than to the economy as a whole. Spatial ABMs at the level of large regions and countries are not yet sufficiently developed, but have good prospects in the future. With increasing availability of geodata and technological development in general the number of applications and coverage of spatial ABM will grow
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Bragin, Alexey. "Algorithm for Generating Random Vectors in Economic models." Artificial societies 19, no. 1 (2024): 0. http://dx.doi.org/10.18254/s207751800029906-5.
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The initial step in constructing a multi-agent population model is the construction of a set of agent objects, the attributes of which will be distributed in accordance with statistical data about the real population, the model of which is to be built. These attributes, depending on the type of model, may include geographic location, social connections, employment, education, and income level, but the common point is that such data are often frequency tables of certain levels of classification of the values of individual traits of individuals in a population. Each table characterizes the distribution of one random variable. Each agent should be assigned attribute values according to these distributions. Unlike other models that have been closely related to specific agent environments, this paper presents a generalized approach that offers a universal tool for assigning agent attributes that can be easily integrated into various modeling scenarios. This stage of modeling can be performed using a generator of pseudo-random vectors. The distribution of element values in each position will correspond to the given frequency tables. The article addresses the issues of developing an algorithm for such a generator, assessing its accuracy and efficiency, as well as demonstrating its application using several examples.
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Trinidad Segovia, Juan E., Fabrizio Di Sciorio, Raffaele Mattera, and Maria Spano. "A Bibliometric Analysis on Agent-Based Models in Finance: Identification of Community Clusters and Future Research Trends." Complexity 2022 (September 15, 2022): 1–11. http://dx.doi.org/10.1155/2022/4741566.
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Agent-based models are computational approaches used to reproduce the interactions between economic agents. These models are widely applied in many contexts to get deeper understanding about agents’ behaviors within complex systems. In this paper, we provide a bibliometric analysis about agent-based models in finance and, considering bibliographic coupling, we identify the presence of two distinct clusters of research communities, i.e., financial economics and econophysics. Cluster-specific thematic analyses are conducted to understand if the two communities are characterized by different emerging and motor topics. By highlighting several differences in the clusters, we also show the two research communities specialized in different specific topics.
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Wolf, Sarah, Jean-Philippe Bouchaud, Federico Cecconi, Silvano Cincotti, Herbert Dawid, Herbert Gintis, Sander van der Hoog, et al. "Describing economic agent-based models – Dahlem ABM documentation guidelines." Complexity Economics 2, no. 1 (January 1, 2013): 63–74. http://dx.doi.org/10.7564/13-coec12.
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McAdams, David. "The Blossoming of Economic Epidemiology." Annual Review of Economics 13, no. 1 (August 5, 2021): 539–70. http://dx.doi.org/10.1146/annurev-economics-082120-122900.
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Infectious diseases, ideas, new products, and other infectants spread in epidemic fashion through social contact. The COVID-19 pandemic, the proliferation of fake news, and the rise of antimicrobial resistance have thrust economic epidemiology into the forefront of public policy debate and reinvigorated the field. Focusing for concreteness on disease-causing pathogens, this review provides a taxonomy of economic-epidemic models, emphasizing both the biology/immunology of the disease and the economics of the social context. An economic epidemic is one whose diffusion through the agent population is generated by agents’ endogenous behavior. I highlight properties of the equilibrium epidemic trajectory and discuss ways in which public health authorities can change the game for the better by ( a) imposing restrictions on agent activity to reduce the harm done during a viral outbreak and ( b) enabling diagnostic-informed interventions to slow or even reverse the rise of antibiotic resistance.
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IRLE, ALBRECHT, JONAS KAUSCHKE, THOMAS LUX, and MISHAEL MILAKOVIĆ. "SWITCHING RATES AND THE ASYMPTOTIC BEHAVIOR OF HERDING MODELS." Advances in Complex Systems 14, no. 03 (June 2011): 359–76. http://dx.doi.org/10.1142/s0219525911002949.
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Markov chains have experienced a surge of economic interest in the form of behavioral agent-based models that aim at explaining the statistical regularities of financial returns. We review some of the relevant mathematical facts and show how they apply to agent-based herding models, with the particular goal of establishing their asymptotic behavior since several studies have pointed out that the ability of such models to reproduce the stylized facts hinges crucially on the size of the agent population (typically denoted by N), a phenomenon that is also known as N-dependence. Our main finding is that N-(in)dependence traces back to both the topology and the velocity of information transmission among heterogeneous financial agents.
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Trimborn, Torsten, Philipp Otte, Simon Cramer, Maximilian Beikirch, Emma Pabich, and Martin Frank. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models." Computational Economics 55, no. 2 (July 22, 2019): 707–44. http://dx.doi.org/10.1007/s10614-019-09910-1.
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Kilyachkov, N. A. "Biological Communities as Economic Subjects." MGIMO Review of International Relations, no. 1(40) (February 28, 2015): 144–52. http://dx.doi.org/10.24833/2071-8160-2015-1-40-144-152.
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When the natural resource management is considered by means of economics, biological associations are only regarded as the source of resources and the object of ownership from the part of economic agents (households, firms or the state). Such approach does not allow studying certain types of situations, like interaction of an economic agent and a natural ecosystem, property rights over which are not well defined, and does not account for system attributes of the environment. This study proposes to consider biological associations (populations or ecosystems) as specific economic subjects. It is shown that these associations regularly face the choice between various alternative uses of scarce means, which constitutes economic choice by definition. Different levels of organization in nature and economy are compared. It is defined at which level biological associations can be regarded as the subjects of economic choice in a way analogous to economic agents. A term «agent of evolutionary process»is coined for such associations. The main similarities between economic and ecological systems are listed, namely: spontaneous character of processes, crucial role of internal factors, expansion as the main criterion of determining winners and losers, high number of agents. Key differences between the two types of systems are indicated, which are: much higher number of alternatives treated by the economic agents and higher speed of reaction of the economic agents to the changes in the environment. The conclusion is made that the models where ecosystems are regarded as acting subjects are possible and useful.
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Azaria, Amos, Zinovi Rabinovich, Sarit Kraus, Claudia Goldman, and Ya'akov Gal. "Strategic Advice Provision in Repeated Human-Agent Interactions (Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 2411–12. http://dx.doi.org/10.1609/aaai.v26i1.8408.
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This paper addresses the problem of automated advice provision in settings that involve repeated interactions between people and computer agents. This problem arises in many real world applications such as route selection systems and office assistants. To succeed in such settings agents must reason about how their actions in the present influence people's future actions. The paper describes several possible models of human behavior that were inspired by behavioral economic theories of people's play in repeated interactions. These models were incorporated into several agent designs to repeatedly generate offers to people playing the game. These agents were evaluated in extensive empirical investigations including hundreds of subjects that interacted with computers in different choice selections processes. The results revealed that an agent that combined a hyperbolic discounting model of human behavior with a social utility function was able to outperform alternative agent designs. We show that this approach was able to generalize to new people as well as choice selection processes that were not used for training. Our results demonstrate that combining computational approaches with behavioral economics models of people in repeated interactions facilitates the design of advice provision strategies for a large class of real-world settings.
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BEIKIRCH, MAXIMILIAN, SIMON CRAMER, MARTIN FRANK, PHILIPP OTTE, EMMA PABICH, and TORSTEN TRIMBORN. "ROBUST MATHEMATICAL FORMULATION AND PROBABILISTIC DESCRIPTION OF AGENT-BASED COMPUTATIONAL ECONOMIC MARKET MODELS." Advances in Complex Systems 23, no. 06 (September 2020): 2050017. http://dx.doi.org/10.1142/s0219525920500174.
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In science and especially in economics, agent-based modeling has become a widely used modeling approach. These models are often formulated as a large system of difference equations. In this study, we discuss two aspects, numerical modeling and the probabilistic description for two agent-based computational economic market models: the Levy–Levy–Solomon model and the Franke–Westerhoff model. We derive time-continuous formulations of both models, and in particular, we discuss the impact of the time-scaling on the model behavior for the Levy–Levy–Solomon model. For the Franke–Westerhoff model, we proof that a constraint required in the original model is not necessary for stability of the time-continuous model. It is shown that a semi-implicit discretization of the time-continuous system preserves this unconditional stability. In addition, this semi-implicit discretization can be computed at cost comparable to the original model. Furthermore, we discuss possible probabilistic descriptions of time-continuous agent-based computational economic market models. Especially, we present the potential advantages of kinetic theory in order to derive mesoscopic descriptions of agent-based models. Exemplified, we show two probabilistic descriptions of the Levy–Levy–Solomon and Franke–Westerhoff model.
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Dobrynin, Alexey Sergeyevich, Alexander Sergeyevich Koynov, and Marina Vladimirovna Purgina. "Agent model for multi-agent organizational system: economic aspect." Vestnik of Astrakhan State Technical University. Series: Economics 2019, no. 4 (December 16, 2019): 14–20. http://dx.doi.org/10.24143/2073-5537-2019-4-14-20.
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Modeling the behavior of social and economic systems plays an important role in various fields of knowledge. An important task is the prediction of the behavior of such systems and their states in the future, for the adoption of proactive and corrective management decisions. Despite the constant development of game theory and other disciplines of a socio-economic (cybernetic) profile, the issue of a generalized formalized description of such systems consisting of active elements remains relevant. Significant difficulties arise with the formal description of any systems that contain active elements (people), due to the human factor and the lack of reliable knowledge about the features of the human brain. Many theories, in particular the theory of management of organizational systems, use the idea of an “economic” person as a hypothesis, whose preferences can be described by the objective cost and effect functions, which is typical for “western” scientific trends. In most cases, the developers of such theories do not propose anything concrete, but merely shift the responsibility to decision makers who are invited to build all the models on their own. It has been stated that so far many social and economic processes are considered as strategic games and zero-sum games, which leaves its mark on the decision-making process. An agent model for multi-agent organizational systems has been formed, which can be used to develop planning, incentive mechanisms that are focused on the needs and wishes of work performers. Elements of the agent model under consideration are implemented in the system for monitoring the effectiveness of the activity of the Siberian State Industrial University.
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Eftonova, Tatyana, Mariam Kiran, and Mike Stannett. "Long-term Macroeconomic Dynamics of Competition in the Russian Economy using Agent- based Modelling." International Journal of System Dynamics Applications 6, no. 1 (January 2017): 1–20. http://dx.doi.org/10.4018/ijsda.2017010101.
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Agent-based economic modelling techniques are increasingly being used to complement standard economic simulations. This paper re-models a standard equation-based simulation model of the Russian macroeconomy in an agent-based setup, and uses it to investigate the effect that antimonopoly legislation can be expected to have upon long-term dynamic behaviour. The results reveal various potential outcomes which would have not been visible using traditional equation-based modelling techniques. While the number of economic agents has been kept deliberately small in the work presented here, the modelling approach is scalable to systems incorporating many millions of agents.
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Azaria, Amos, Zinovi Rabinovich, Sarit Kraus, Claudia Goldman, and Ya'akov Gal. "Strategic Advice Provision in Repeated Human-Agent Interactions." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 1522–28. http://dx.doi.org/10.1609/aaai.v26i1.8338.
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This paper addresses the problem of automated advice provision in settings that involve repeated interactions between people and computer agents. This problem arises in many real world applications such as route selection systems and office assistants. To succeed in such settings agents must reason about how their actions in the present influence people's future actions. This work models such settings as a family of repeated bilateral games of incomplete information called ``choice selection processes'', in which players may share certain goals, but are essentially self-interested. The paper describes several possible models of human behavior that were inspired by behavioral economic theories of people's play in repeated interactions. These models were incorporated into several agent designs to repeatedly generate offers to people playing the game. These agents were evaluated in extensive empirical investigations including hundreds of subjects that interacted with computers in different choice selections processes. The results revealed that an agent that combined a hyperbolic discounting model of human behavior with a social utility function was able to outperform alternative agent designs, including an agent that approximated the optimal strategy using continuous MDPs and an agent using epsilon-greedy strategies to describe people's behavior. We show that this approach was able to generalize to new people as well as choice selection processes that were not used for training. Our results demonstrate that combining computational approaches with behavioral economics models of people in repeated interactions facilitates the design of advice provision strategies for a large class of real-world settings.
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Chen, Shu-Heng. "Neuroeconomics and Agent-Based Computational Economics." International Journal of Applied Behavioral Economics 3, no. 2 (April 2014): 15–34. http://dx.doi.org/10.4018/ijabe.2014040102.
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Recently, the relation between neuroeconomics and agent-based computational economics (ACE) has become an issue concerning the agent-based economics community. Neuroeconomics can interest agent-based economists when they are inquiring for the foundation or the principle of the software-agent design. It has been shown in many studies that the design of software agents is non-trivial and can determine what will emerge from the bottom. Therefore, it has been quested for rather a period regarding whether anyone can sensibly design these software agents, including both the choice of software agent models, such as reinforcement learning, and the parameter setting associated with the chosen model, such as risk attitude. In this paper, the author will start a formal inquiry by focusing on examining the models and parameters used to build software agents.
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GILBERT, NIGEL. "OPEN PROBLEMS IN USING AGENT-BASED MODELS IN INDUSTRIAL AND LABOR DYNAMICS." Advances in Complex Systems 07, no. 02 (June 2004): 285–88. http://dx.doi.org/10.1142/s0219525904000093.
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The preceding papers have shown the impressive versatility and potential of agent-based modeling in developing an understanding of industrial and labor dynamics. The main attraction of agent-based models is that the actors — firms, workers, and networks — that are the objects of study in the 'real world,' can be represented directly in the model. This one-to-one correspondence between model agents and economic actors provides greater clarity and more opportunities for analysis than many alternative modeling approaches. However, the advantages of agent-based modeling have to be tempered by disadvantages and as yet unsolved methodological problems. In this brief summary drawn from the discussion at the closing session of WILD@ACE, we review three of these open problems in the context of the papers presented at the conference: How can agent-based models be empirically validated? What criteria should be used to evaluate the explanatory success of agent-based models? And how can the conclusions of research on similar topics be integrated?
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Kaplan, Greg, and Giovanni L. Violante. "Microeconomic Heterogeneity and Macroeconomic Shocks." Journal of Economic Perspectives 32, no. 3 (August 1, 2018): 167–94. http://dx.doi.org/10.1257/jep.32.3.167.
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In this essay, we discuss the emerging literature in macroeconomics that combines heterogeneous agent models, nominal rigidities, and aggregate shocks. This literature opens the door to the analysis of distributional issues, economic fluctuations, and stabilization policies—all within the same framework. In response to the limitations of the representative agent approach to economic fluctuations, a new framework has emerged that combines key features of heterogeneous agents (HA) and New Keynesian (NK) economies. These HANK models offer a much more accurate representation of household consumption behavior and can generate realistic distributions of income, wealth, and, albeit to a lesser degree, household balance sheets. At the same time, they can accommodate many sources of macroeconomic fluctuations, including those driven by aggregate demand. In sum, they provide a rich theoretical framework for quantitative analysis of the interaction between cross-sectional distributions and aggregate dynamics. In this article, we outline a state-of-the-art version of HANK together with its representative agent counterpart, and convey two broad messages about the role of household heterogeneity for the response of the macroeconomy to aggregate shocks: 1) the similarity between the Representative Agent New Keynesian (RANK) and HANK frameworks depends crucially on the shock being analyzed; and 2) certain important macroeconomic questions concerning economic fluctuations can only be addressed within heterogeneous agent models.
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Abramov, V. I., A. N. Kudinov, and D. S. Evdokimov. "Application of social modeling using agent based approach in scientific and technical development, implementation of R&D and maintenance of innovative potential." Proceedings of the Voronezh State University of Engineering Technologies 81, no. 3 (December 20, 2019): 339–59. http://dx.doi.org/10.20914/2310-1202-2019-3-339-359.
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Agent based models (ABM) and multiagent systems (MAS) can be used to solve problems in many fields of research - from natural and computer to economics and social sciences. Many natural and social phenomena can be represented in form of complex simulations so over time agent models and multi-agent systems have proven to be a really powerful tool in areas such as economics and trade, health, urban planning and social sciences. In addition multi-agent systems can be represented as an artificial society similar to a human one and consisting of entities with characteristics similar to human ones, for example in terms of autonomy and intelligence. ABM are based on the principle of objective orientation as well as the evolution (training) of agents in the process of modeling various variants of the proposed events. Despite the apparent simplicity of the rules of interaction between agents the results are usually non-obvious and quite meaningful. ABM can be developed both at the micro level and represent models with multiple agents at the macro level. The concept of multi-agent systems which immediately gained followers and support in both scientific circles and industrial communities, first started talking in the mid-1980s. Over the past thirty years, the methodology of IAU creation has been constantly improved: technologies and tools for its promotion and use in the management of large-scale network structures (such as defense systems, energy, health, transport, logistics, urban management, collective robotics, etc.) have been actively developed. The scope of application of MAS is very wide. The analysis of implemented MAS proves that currently the tool is the most advanced technology for managing any objects built on the principles of self-organization. However, despite all the evidence of positive prospects for the introduction of AOM technology the number of examples of its successful application to date is small. In this regard creation of new platforms for discussion of international experience and improvement of the approach to simulation modeling in general is especially important for further dissemination of AMB and MAS. Creation of an open consortium for agent-oriented modeling as well as promotion of development, communication and dissemination of research results as well as implementation of educational activities together will contribute to the development of agent based modeling. The analysis and review of existing methodology of social modeling with use of agent based approach in the application to scientific and technical development, implementation of R&D and maintenance of innovative potential showed that models characterized by complex multi-level processes and interactions of agents have more capacious software structures which depend more on the "fine" tuning of the agents themselves. Such models can contain and use a voluminous set of data, and in the field of economic research tend to focus on the analysis and forecasting of various socio-economic processes at the macro level.
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Cincotti, Silvano, Marco Raberto, and Andrea Teglio. "Why do we need agent-based macroeconomics?" Review of Evolutionary Political Economy 3, no. 1 (March 29, 2022): 5–29. http://dx.doi.org/10.1007/s43253-022-00071-w.
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AbstractWe are entering the third decade of the twenty-first century with profound uncertainties and crucial challenges for the world economy. Phenomena like climate change, digital transformation, migration, demographic changes, and the ongoing COVID pandemic need to be understood and promptly addressed. We argue that the agent-based approach in economics is well suited to tackle these topics, because of its capacity to integrate the “micro” and “macro” dimensions by modelling the network of interactions among heterogeneous economic agents and their aggregate outcomes. This paper explains why the agent-based methodology is needed to overcome the limitations of the neoclassical approach in economics, which has not been able to properly address those challenges. To do so, the paper retraces the main stages of the scientific evolution in a general historical and epistemological perspective, showing how the paradigm of reductionism, which led to extraordinary advances after the scientific revolution of the seventeenth century, is less effective when addressing the main challenges ahead. On the other hand, the sciences of chaos theory and complex systems can provide the economic discipline with more suitable instruments to face those challenges. Finally, the paper briefly presents the contributions of the special issue, which use applications of agent-based models to study the main problems of our times.
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Kaplan, Greg, and Giovanni L. Violante. "The Marginal Propensity to Consume in Heterogeneous Agent Models." Annual Review of Economics 14, no. 1 (August 12, 2022): 747–75. http://dx.doi.org/10.1146/annurev-economics-080217-053444.
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What model features and calibration strategies yield a large average marginal propensity to consume (MPC) in heterogeneous agent models? Through a systematic investigation of models with different preferences, dimensions of ex-ante heterogeneity, income processes, and asset structures, we show that the most important factor is the share and type of hand-to-mouth households. One-asset models either feature a trade-off between a high average MPC and a realistic level of aggregate wealth or generate an excessively polarized wealth distribution that vastly understates the wealth held by households in the middle of the distribution. Two-asset models that include both liquid and illiquid assets can resolve this tension with a large enough gap between liquid and illiquid returns. We discuss how such return differential can be justified from the perspective of theory and data.
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Arellano, Manuel, and Stéphane Bonhomme. "Nonlinear Panel Data Methods for Dynamic Heterogeneous Agent Models." Annual Review of Economics 9, no. 1 (August 2, 2017): 471–96. http://dx.doi.org/10.1146/annurev-economics-063016-104346.
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Evdokimov, Dmitry. "Approaches to Assessing the Socio-Economic Consequences of the COVID-19 Pandemic Using Computer Simulation." Artificial societies 17, no. 3 (2022): 0. http://dx.doi.org/10.18254/s207751800021929-0.
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In the context of the coronavirus pandemic, there is an increasing need to develop methods for scientifically based assessment of the consequences both at the level of the country's economy and at the regional level. One of the acute problems of the development of the Russian economy in the context of the coronavirus pandemic is the conflict between measures to protect the life and health of people and the fall in economic activity. To support the economy, countries are taking anti-crisis measures, which are aimed primarily at overcoming serious consequences in the most vulnerable sectors. As part of the study, to assess the socio-economic consequences of the epidemic and reproduce forecasts, modern simulation tools are used - agent-based modeling. Agent-based models allow you to use software of various classes, including neural networks, mathematical models, 3D-4D add-ons and other technologies that can visualize the results of scenario predictive estimates and computational experiments. The aim of the study is to develop methods and techniques for forecasting and scenario modeling of the socio-economic consequences of viral epidemics. For the study, a detailed statistical and analytical database was formed, adaptive blocks were developed with the possibility of additional inclusion of indicators. The software implementation included three functional blocks: demographic, economic and epidemiological, as well as three categories of agents within each subject of the Russian Federation with individual characteristics based on accepted world practice. The software tool chosen to implement the research objectives is the platform for creating agent-based models "AnyLogic". The study was carried out on the example of the following subjects of the Russian Federation: Murmansk region, Krasnodar region, Sverdlovsk, Samara and Voronezh regions. Based on the results of the study, an architecture of an agent-based model was developed, which makes it possible to evaluate restrictive measures and regulations in terms of the socio-economic consequences of a pandemic. As a result of the study, methods and algorithms for agent-based modeling of the socio-economic consequences of viral epidemics were developed, taking into account spatial and communicative interactions. To fulfill the objectives of the study, at the first stage, an analysis of scientific methods for forecasting and building various models for assessing the consequences of macroeconomic decisions and models for the spread of viral epidemics was carried out. At the second stage, an agent-based model was developed, which took into account structured and unstructured information, including the socio-demographic and economic characteristics of the regions, such as morbidity and mortality, employment rates, as well as measures taken by the regions to counter the spread of COVID-19. In terms of social interaction between agents, the study implemented a dynamic multi-relational (MRN) social network of agents, the structure of which changes during the introduction of quarantine measures that limit the degree of interaction between them. The introduction of different specific values of individual characteristics within a population of agents of the same type makes it possible to assess the socio-economic consequences of viral epidemics with the maximum degree of detail - at the level of individuals. Further development of this area of research will include refinement of the developed model for analyzing the consequences of the spread of viral epidemics in terms of the socio-economic development of territorial systems based on the obtained forecast scenarios.
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Beklaryan, Gayane. "Simulation modelling of multi-agent regional socio-economic systems: methods and examples." Herald of CEMI 6, no. 4 (2023): 0. http://dx.doi.org/10.33276/s265838870029157-5.
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The paper presents a new approach to modelling multi-agent regional socio-economic systems. Within the framework of the proposed approach, the possibilities of constructing hybrid multi-level simulation models using methods system dynamics and agent-based modelling, designed to assess the state of individual regions, characterizing the economic growth rate (e. g., the GRP dynamics) and finding solutions to improve them. As a result, it is possible to study the characteristics of the regional socio-economic system, taking into account the complex relationships and interactions between economic agents and the external environment. At the same time, the use of the simulation modelling tools such as the AnyLogic makes it possible to implement a decision-making system for regional socio-economic planning as the software framework.
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Kirman, Alan. "Learning in Agent-based Models." Eastern Economic Journal 37, no. 1 (December 28, 2010): 20–27. http://dx.doi.org/10.1057/eej.2010.60.
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Горбанева, Ольга Ивановна, and Olga Gorbaneva. "Corruption mechanisms in models of social and private interests combining engine in the case of one agent. Optimization approach." Mathematical Game Theory and Applications 12, no. 2 (December 23, 2020): 36–62. http://dx.doi.org/10.17076/mgta_2020_2_15.
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The paper is devoted to the investigation of corruption in models of social and private interests combining (SPICE-models) in the case of one agent. The specific attention in the article is given to the optimization approach investigation. In the structure of model between the higher level (principal) and the lower levels (agents) element "supervisor" is included. Supervisor acts in interests of principal, but he can weaken principal's demands for agent in exchange of a bribe. Administrative and economic corruption mechanisms are introduced and investigated. Optimization approach is applied.
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Ageeva, Alina. "Budget Processes Modeling: Agent-Based Approach." Artificial societies 18, no. 2 (2023): 0. http://dx.doi.org/10.18254/s207751800025645-8.
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The financial and economic crisis caused by anti-Russian sanctions and geopolitical tensions necessitates an increase in budget expenditures at all levels, in connection with which the issues of improving the fiscal system and reorganizing interbudgetary relations are being updated. To develop plans for adapting to current challenges, achieving the goals of fiscal stability and improving economic performance, transformations in the field of budget processes, the implementation of an effective fiscal policy, and a rethinking of the principles of fiscal federalism are necessary. Achieving this goals predetermines the need to use flexible budget analysis tools, such as agent-based modeling, which allows to reproduce the multi-level structure of the country's economy and budget system, as well as simulate complex budget processes. The article presents an overview of agent-based models developed by foreign and Russian researchers that reproduce various aspects of budgetary processes. The analysis of the architecture of models was carried out and the conceptual ideas of building models in accordance with the types of agents, their behavioral algorithms, as well as the goals of simulation modeling were considered. The expediency and efficiency of using the agent-based approach for modeling complex budgetary processes is substantiated. The analysis carried out in the article allows us to conclude that the agent-based simulation approach is successfully used for the following purposes: testing variable strategies for reforming budgetary processes, predicting the consequences of implementing fiscal policy, choosing the optimal strategy aimed at achieving budgetary self-sufficiency of the regions, as well as creating tools for assessing the effective allocation of budgetary funds to achieve target variables. The results of the study are proposed to be used for agent-based modeling of sanctions effects and search for strategies for reforming the fiscal system in order to counter the financial and economic crisis.
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Bureš, Vladimír, and Petr Tučník. "Complex agent-based models: application of a constructivism in the economic research." E+M Ekonomie a Management 17, no. 3 (September 4, 2014): 152–68. http://dx.doi.org/10.15240/tul/001/2014-3-012.
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Dawid, Herbert, and Giorgio Fagiolo. "Agent-based models for economic policy design: Introduction to the special issue." Journal of Economic Behavior & Organization 67, no. 2 (August 2008): 351–54. http://dx.doi.org/10.1016/j.jebo.2007.06.009.
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Cifuentes-Faura, Javier, and Renaud Di Francesco. "Nanoeconomics of Households in Lockdown Using Agent Models during COVID-19." Sustainability 14, no. 4 (February 11, 2022): 2083. http://dx.doi.org/10.3390/su14042083.
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The world is experiencing a global pandemic with COVID-19, for which few measures have proven their efficiency. Prevention through lockdown belongs to the portfolio of Non-Pharmaceutical Intervention (NPI). The implementation of a lockdown comes with a potential health care benefit balanced with an economic and human cost: people are constrained to stay in their homes. Households hence have to live together in what we call “zero-space”, which means within the walls of their flat or house. The loss of “space-domain” freedom, preventing them to move in “free” space is accompanied by a continued “time-domain” freedom with the possibility to allocate their time, and what they do with it, within the location they are not permitted to leave (with very defined exceptions). We study the microeconomics framework in such a setting, starting from the rules shaping such a “nano-market” with very few agents (the members of the household), and its consequence for nano-economic interaction. Since the behaviour of the agents is hyperconstrained in the space domain and relatively free in the time domain, behavioral economics is used to describe decisions made in the home, for the actions remaining possible during lockdown. A minimal set of rules is introduced and illustrated to describe efficiently the agents at play in this new and particular context, which has been replicated worldwide during the pandemic. Hypotheses for this model are presented and discussed, so as to allow future variations and adaptations for other specific cases with different options chosen. Such hypotheses concern agents, their interests, behaviours, and the equivalent of non-financial “nano-market transactions and contracts”.
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Xia, Wei Li, Xing Ji Wei, and Tong Tong Sun. "Applied-Information Technology in Multi-Agent Simulation in Securities Market Modeling." Applied Mechanics and Materials 707 (December 2014): 501–4. http://dx.doi.org/10.4028/www.scientific.net/amm.707.501.
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Multi-agent simulation could make a contribution to a better understanding of social and economic systems. To some extent, multi-agent simulation can be seen as experimental technique for hypothesis testing and scenario analysis, which can be used complementary and in combination with experiments in social and economic systems. The multi-agent model represent a broad class of models which have been introduced to describe the economic dynamics in a more realistic way. Their building blocks are: the agents are heterogeneous with respect to their various properties like strategies, wealth, time scale,etc; the interaction between them is a fundamental element and, of course, it can have many different characteristics; price dynamics depends on the balance between offer and demand but the specific implementation can be different. In the framework of applied-information technology of microscopic simulation, a multi-agent model of the stock exchange is developed for the case of one type of shares.
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Zehra, Ayesha, and Amena Urooj. "A Bibliometric Analysis of the Developments and Research Frontiers of Agent-Based Modelling in Economics." Economies 10, no. 7 (July 19, 2022): 171. http://dx.doi.org/10.3390/economies10070171.
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Dynamic Stochastic General Equilibrium (DSGE) models are widely used as a tool for policy decision-making. These models lost their fame when they could not predict the crisis in 2008 and could not address policy problems afterward. Meanwhile, the Agent-Based Modelling (ABM) approach emerged as an alternative to DSGE models. Between 2000 and 2020, this study examined scholarly research on the topic of ABM in economics. The information is gathered using the SCOPUS database. Numerous bibliometric indicators are provided, including the total number of publications and citations. The study reveals that agent-based modelling in economics research has grown in recent years. The majority of active research occurs in countries such as the United States of America, and collaboration allows researchers to reach out to many more countries. ABM has the potential to be applied in a wide range of economic fields. ABM also necessitates research into its own development to be used to better understand economic phenomena.
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ZGONNIKOV, ARKADY, and IHOR LUBASHEVSKY. "UNSTABLE DYNAMICS OF ADAPTATION IN UNKNOWN ENVIRONMENT DUE TO NOVELTY SEEKING." Advances in Complex Systems 17, no. 03n04 (July 2014): 1450013. http://dx.doi.org/10.1142/s0219525914500131.
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Learning and adaptation play great role in emergent socio-economic phenomena. Complex dynamics has been previously found in the systems of multiple learning agents interacting via a simple game. Meanwhile, the single agent adaptation is considered trivially stable. We advocate the idea that adopting a more complex model of the individual behavior may result in a more diverse spectrum of macro-level behaviors. We develop an adaptation model based on the reinforcement learning framework extended by an additional processing channel. We scrutiny the dynamics of the single agent adapting to the unknown environment; the agent is biased by novelty seeking, the intrinsic inclination for exploration. We demonstrate that the behavior of the novelty-seeking agent may be inherently unstable. One of the surprising results is that under certain conditions the increase of the novelty-seeking level may cause the agent to switch from the non-rational to the strictly rational behavior. Our results give evidence to the hypothesis that the intrinsic motives of agents should be paid no less attention than the extrinsic ones in the models of complex socio-economic systems.
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Shaaban, Mostafa, Jürgen Scheffran, Mohamed Salah Elsobki, and Hossein Azadi. "A Comprehensive Evaluation of Electricity Planning Models in Egypt: Optimization versus Agent-Based Approaches." Sustainability 14, no. 3 (January 28, 2022): 1563. http://dx.doi.org/10.3390/su14031563.
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A rational assessment of electricity generation technologies constitutes a cornerstone to attain a sustainable and secure electricity plan. The Egyptian government is struggling with the accelerated growth of the national electricity demand through setting up and examining different future electricity scenarios and through the implementation of energy models to secure the provision of affordable and clean energy as part of the United Nations 2030 agenda of achieving the 17 sustainable development goals (SDGs). However, conventional techno-economic models still represent for many countries an attractive tool for energy planning. We investigate in this article the added values of applying a dynamic multi-criteria spatial-agent model that covers several sustainability dimensions versus an optimization techno-economic model for future energy planning in Egypt. Moreover, we report on the historical development of electricity supply since 2009 in Egypt. The study reveals predominant advantages of applying the agent-based modeling approach, which simulates the evolution of an energy transition landscape through the interactive and adaptive dynamic decision behavior of different societal groups (agents) in response to changes in the whole system. The study advocates the implementation of a dynamic agent-based bottom-up approach for the planning of a future sustainable electricity mix in Egypt.
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Brown, Calum, Ian Holman, and Mark Rounsevell. "How modelling paradigms affect simulated future land use change." Earth System Dynamics 12, no. 1 (February 23, 2021): 211–31. http://dx.doi.org/10.5194/esd-12-211-2021.
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Abstract. Land use models operating at regional to global scales are almost exclusively based on the single paradigm of economic optimisation. Models based on different paradigms are known to produce very different results, but these are not always equivalent or attributable to particular assumptions. In this study, we compare two pan-European integrated land use models that utilise the same climatic and socio-economic scenarios but which adopt fundamentally different modelling paradigms. One of these is a constrained optimising economic-equilibrium model, and the other is a stochastic agent-based model. We run both models for a range of scenario combinations and compare their projections of spatially aggregate and disaggregate land use changes and ecosystem service supply levels in food, forest and associated environmental systems. We find that the models produce very different results in some scenarios, with simulated food production varying by up to half of total demand and the extent of intensive agriculture varying by up to 25 % of the EU land area. The agent-based model projects more multifunctional and heterogeneous landscapes in most scenarios, providing a wider range of ecosystem services at landscape scales, as agents make individual, time-dependent decisions that reflect economic and non-economic motivations. This tendency also results in food shortages under certain scenario conditions. The optimisation model, in contrast, maintains food supply through intensification of agricultural production in the most profitable areas, sometimes at the expense of land abandonment in large parts of Europe. We relate the principal differences observed to underlying model assumptions and hypothesise that optimisation may be appropriate in scenarios that allow for coherent political and economic control of land systems, but not in scenarios in which economic and other scenario conditions prevent the changes in prices and responses required to approach economic equilibrium. In these circumstances, agent-based modelling allows explicit consideration of behavioural processes, but in doing so it provides a highly flexible account of land system development that is harder to link to underlying assumptions. We suggest that structured comparisons of parallel and transparent but paradigmatically distinct models are an important method for better understanding the potential scope and uncertainties of future land use change, particularly given the substantive differences that currently exist in the outcomes of such models.
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AYLETT, RUTH, KERSTIN DAUTENHAHN, JIM DORAN, MICHAEL LUCK, SCOTT MOSS, and MOSHE TENNENHOLTZ. "Can models of agents be transferred between different areas?" Knowledge Engineering Review 15, no. 2 (June 2000): 197–203. http://dx.doi.org/10.1017/s0269888900002034.
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One of the main reasons for the sustained activity and interest in the field of agent-based systems, apart from the obvious recognition of its value as a natural and intuitive way of understanding the world, is its reach into very many different and distinct fields of investigation. Indeed, the notions of agents and multi-agent systems are relevant to fields ranging from economics to robotics, in contributing to the foundations of the field, being influenced by ongoing research, and in providing many domains of application. While these various disciplines constitute a rich and diverse environment for agent research, the way in which they may have been linked by it is a much less considered issue. The purpose of this panel was to examine just this concern, in the relationships between different areas that have resulted from agent research. Informed by the experience of the participants in the areas of robotics, social simulation, economics, computer science and artificial intelligence, the discussion was lively and sometimes heated.
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POSADA, MARTA, CESÁREO HERNÁNDEZ, and ADOLFO LÓPEZ-PAREDES. "TESTING MARSHALLIAN AND WALRASIAN INSTABILITY WITH AN AGENT-BASED MODEL." Advances in Complex Systems 11, no. 02 (April 2008): 249–60. http://dx.doi.org/10.1142/s021952590800160x.
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Do the Marshallian and Walrasian models have a firm foundation on micromotives, or are they just macroabstractions that we could dispense in microeconomics? Previous evidence from experimental economics with human subjects in continuous double auction markets shows that, if the supply is downward-sloping or the demand is upward-sloping, the Marshallian stability model captures the observed phenomena but the Walrasian does not. But, of course, in human subjects experiments the basic question of what the human agents' behavior is remains open. We build an artificial agent-based model to show that the path of convergence needs more intelligence than the zero-intelligence framework. Results from experimental economics with human agents reproduced with artificial agents at a low cost and with an increase in the reliability of the experiment. Not only the existence but also the computation of the stability is relevant. These results cast a shadow on the interest of both instability concepts in economic theory.
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Grames, J., A. Prskawetz, D. Grass, and G. Blöschl. "Modelling the interaction between flooding events and economic growth." Proceedings of the International Association of Hydrological Sciences 369 (June 11, 2015): 3–6. http://dx.doi.org/10.5194/piahs-369-3-2015.
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Abstract. Socio-hydrology describes the interaction between the socio-economy and water. Recent models analyze the interplay of community risk-coping culture, flooding damage and economic growth (Di Baldassarre et al., 2013; Viglione et al., 2014). These models descriptively explain the feedbacks between socio-economic development and natural disasters like floods. Contrary to these descriptive models, our approach develops an optimization model, where the intertemporal decision of an economic agent interacts with the hydrological system. In order to build this first economic growth model describing the interaction between the consumption and investment decisions of an economic agent and the occurrence of flooding events, we transform an existing descriptive stochastic model into an optimal deterministic model. The intermediate step is to formulate and simulate a descriptive deterministic model. We develop a periodic water function to approximate the former discrete stochastic time series of rainfall events. Due to the non-autonomous exogenous periodic rainfall function the long-term path of consumption and investment will be periodic.
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Bernardi, Emanuele, Lorenzo Pareschi, Giuseppe Toscani, and Mattia Zanella. "Effects of Vaccination Efficacy on Wealth Distribution in Kinetic Epidemic Models." Entropy 24, no. 2 (January 29, 2022): 216. http://dx.doi.org/10.3390/e24020216.
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The spread of the COVID-19 pandemic has highlighted the close link between economics and health in the context of emergency management. A widespread vaccination campaign is considered the main tool to contain the economic consequences. This paper will focus, at the level of wealth distribution modeling, on the economic improvements induced by the vaccination campaign in terms of its effectiveness rate. The economic trend during the pandemic is evaluated, resorting to a mathematical model joining a classical compartmental model including vaccinated individuals with a kinetic model of wealth distribution based on binary wealth exchanges. The interplay between wealth exchanges and the progress of the infectious disease is realized by assuming, on the one hand, that individuals in different compartments act differently in the economic process and, on the other hand, that the epidemic affects risk in economic transactions. Using the mathematical tools of kinetic theory, it is possible to identify the equilibrium states of the system and the formation of inequalities due to the pandemic in the wealth distribution of the population. Numerical experiments highlight the importance of the vaccination campaign and its positive effects in reducing economic inequalities in the multi-agent society.
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Akopov, Andranick. "Optimization of behaviour strategies within the simulation model of a multi-agent socio-economic system." Economics and the Mathematical Methods 59, no. 3 (2023): 117. http://dx.doi.org/10.31857/s042473880027006-5.
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This article presents a new simulation model of a multi-agent socio-economic system (MA-SES), in which individual, including interproduct interactions are implemented. Within the MA-SES, the models of agent-producers and agents-consumers behaviour with their objective functions are studied. In particular, producers follow their own production strategies by choosing the moments for introducing new fixed assets and labour resources. Consumers participate in barter and monetary deals by interacting with other agents. The states of readiness of agents-producers to introduce new fixed assets and labour resources, as well as the states of readiness of agents-consumers to complete barter and monetary deals are set up for each time moment using lognormal distributions with given characteristics, which are the control parameters of the system. Important bi-objective optimisation problems are formulated for ensembles of agents-producers and agents-consumers. A new parallel hybrid genetic algorithm (MORCGA-MOPSO) was developed, in particular, providing the possibility of seeking the Pareto-optimal solutions for maximising the average (over an ensemble of agents) profit and the total number of agent-producers’ buyers, as well as maximising the average utility and monetary savings of agents-consumers. The parameters of log-normal distributions that determine the states of interacting agents that make individual decisions are computed. The features of the behavioural strategies of producers and consumers are determined, which make possible to achieve an improvement in the values of the objective functions through controlling the dynamics of the input of production resources and choosing the preferred types of interproduct interactions, in particular, barter, monetary etc.
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Tsiatsios, Georgios Alkis, John Leventides, Evangelos Melas, and Costas Poulios. "A bounded rational agent-based model of consumer choice." Data Science in Finance and Economics 3, no. 3 (2023): 305–23. http://dx.doi.org/10.3934/dsfe.2023018.
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<abstract><p>In a bottom-up approach, agent-based models have been extensively used in finance and economics in order to understand how macro-level phenomena can emerge from myriads of micro-level behaviours of individual agents. Moreover, in the absence of (big) data there is still the need to test economic theories and understand how macro-level laws can be materialized as the aggregate of a multitude of interactions of discrete agents. We exemplify how we can solve this problem in a particular instance: We introduce an agent-based method in order to generate data with Monte Carlo and then we interpolate the data with machine learning methods in order to derive multi-parametric demand functions. In particular, the model we construct is implemented in a simulated economy with 1000 consumers and two products, where each consumer is characterized by a unique set of preferences and available income. The demand for each product is determined by a stochastic process, incorporating the uncertainty in consumer preferences. By interpolating the data for the demands for various scenarios and types of consumers we derive poly-parametric demand functions. These demand functions are partially in tension with classical demand theory since on certain occasions they imply that the demand of a product increases as its price increases. Our proposed method of generating data from discrete agents with Monte Carlo and of interpolating the data with machine learning methods can be easily generalized and applied to the assessment of economic theories and to the derivation of economic laws in a bottom-up approach.</p></abstract>
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Huang, Jiangyan, Youkai Cui, Lele Zhang, Weiping Tong, Yunyang Shi, and Zhiyuan Liu. "An Overview of Agent-Based Models for Transport Simulation and Analysis." Journal of Advanced Transportation 2022 (February 27, 2022): 1–17. http://dx.doi.org/10.1155/2022/1252534.
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This article presents an overview of the agent-based modeling and simulation approach and its recent developments in transport fields, with the purpose of discovering the advantages and gaps and encouraging more valuable investigations and applications of agent-based models. We clarify the agent-based model from agents, the background of development, and the basic structure applied in transport systems. Then, the agent-based transport modeling toolkits are discussed. The applications of agent-based models in transport systems are reviewed in three time scale models followed by an additional discussion of hybrid modeling approaches. The extensive modeling of the beliefs, desires, learning, and adaptability of individuals and the optimization problems using agent-based models are explored. Besides, we point out some limitations in terms of calibration and validation procedure, agents’ behavior modeling, and computing efficiency. In conclusion, some recommendations are given and suggest potential and insightful directions such as Big Data and Digital Twin for future research.
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Tsyplakov, Alexander A., and Larisa V. Melnikova. "Fixed Investments and Macroeconomic Agent-Based Modeling." World of Economics and Management 21, no. 1 (2021): 5–28. http://dx.doi.org/10.25205/2542-0429-2021-21-1-5-28.
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The significant progress observed in the field of artificial economy opens up new possibilities for modeling economic growth. Agent-based models (ABM) allow leaving the concept of a representative agent in the past and linking investment decisions of economic agents at the micro level with long-term macroeconomic growth. Modern ABMs offer new algorithms for modeling expectations, agent interaction, technical progress, pricing, and production planning. Our article analyzes the current state of modeling investment in fixed assets in operating macroeconomic ABMs. The subject of the review is the families of models Eurace, CATS, KS, Jamel, Lagom. The authors also present the investment block of the agent-based multiregional input-output model (ABMIOM) being developed. Comparative analysis demonstrates that modern ABMs, as a rule, implement the principle of stock-flow consistency. Modeling the investment process requires detailing the commodity nomenclature, so that the initially adopted two-sector division into investment and consumer goods is replaced by more detailed structures, which gives rise to the problem of accounting for inter-sectoral relations in production and consumption. The Leontief production function copes with this problem, which is confirmed by its widespread use in ABM. The size of firms' investments is often derived from the need to expand capacity in accordance with the current production plan, so that planning turns out to be myopic, and long-term aspects in ABM are still largely unrealized. Nevertheless, already now ABMs reproduce many phenomena associated with the economic cycle. The developed ABMIOM provides horizontal consistency of cash flows between agents and analysis of results using input-output tables. ABMIOM represents a step forward in reflecting intersectoral and interregional flows. The model reproduces the growth and contraction of the economy as a result of independent investment decisions of individual firms and households, which is reflected in the sectoral and spatial structure of the economy. Further development of ABMIOM is associated with the modeling of savings, intrafirm finance, money market, innovation and technical progress.
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Semenov, Sergey P., Viktor V. Slavsky, Maria V. Kurkina, Artyom O. Tashkin, Olga V. Samarina, and Anton A. Finogenov. "Computer mathematical models of socio-economic systems using gis technologies." Yugra State University Bulletin 60, no. 1 (December 23, 2021): 79–84. http://dx.doi.org/10.17816/byusu20210179-84.
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The article describes the results of five years of research in the development and creation of computer mathematical models of socio-economic systems using GIS technologies within the framework of two RFBR grants, which were directed by V.V. Slavsky. The first part describes the results of the development and creation of an interactive socially-oriented geographic information system designed to obtain information about social infrastructure facilities aimed at meeting the needs of people with limited mobility and people with disabilities. The second part describes the results of the development and creation of an interactive agent-based dynamic model of socio-economic processes (transport, production, demographic, etc.), using GIS technologies on the example of Khanty-Mansiysk.
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Xie, Jun, and Min Hua Wu. "Resource Allocation for Parallel Task in Grids." Advanced Materials Research 181-182 (January 2011): 866–72. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.866.
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Task running in Grids may require multiple types of resources simultaneously. Proposing and designing a resource discovery scheme based on Economic Agent. Base on the economic model and the technique in agent of grouping nodes sharing similar files to improve efficiency, this thesis suggests a resource discovery scheme based on economic agent, which is called EAGRD. Theoretical models on resource discovery are provided, under which EAGRD is compared with existing schemes theoretically. By controlling propagation of message into related communities, EAGRD improves time and network efficiency at the cost of topological maintenance overhead.
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Cui, Lijie, and Chuandong Lin. "Lattice–Gas–Automaton Modeling of Income Distribution." Entropy 22, no. 7 (July 17, 2020): 778. http://dx.doi.org/10.3390/e22070778.
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A simple and effective lattice–gas–automaton (LGA) economic model is proposed for the income distribution. It consists of four stages: random propagation, economic transaction, income tax, and charity. Two types of discrete models are introduced: two-dimensional four-neighbor model (D2N4) and D2N8. For the former, an agent either remains motionless or travels to one of its four neighboring empty sites randomly. For the latter, the agent may travel to one of its nearest four sites or the four diagonal sites. Afterwards, an economic transaction takes place randomly when two agents are located in the nearest (plus the diagonal) neighboring sites for the D2N4 (D2N8). During the exchange, the Matthew effect could be taken into account in the way that the rich own a higher probability of earning money than the poor. Moreover, two kinds of income tax models are incorporated. One is the detailed taxable income brackets and rates, and the other is a simplified tax model based on a fitting power function. Meanwhile, charity is considered with the assumption that a richer agent donates a part of his income to charity with a certain probability. Finally, the LGA economic model is validated by using two kinds of benchmarks. One is the income distributions of individual agents and two-earner families in a free market. The other is the shares of total income in the USA and UK, respectively. Besides, impacts of the Matthew effect, income tax and charity upon the redistribution of income are investigated. It is confirmed that the model has the potential to offer valuable references for formulating financial laws and regulations.
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JONG, STEVEN DE, KARL TUYLS, and KATJA VERBEECK. "Fairness in multi-agent systems." Knowledge Engineering Review 23, no. 2 (June 2008): 153–80. http://dx.doi.org/10.1017/s026988890800132x.
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AbstractMulti-agent systems are complex systems in which multiple autonomous entities, called agents, cooperate in order to achieve a common or personal goal. These entities may be computer software, robots, and also humans. In fact, many multi-agent systems are intended to operate in cooperation with or as a service for humans. Typically, multi-agent systems are designed assuming perfectly rational, self-interested agents, according to the principles of classical game theory. Recently, such strong assumptions have been relaxed in various ways. One such way is explicitly including principles derived from human behavior. For instance, research in the field of behavioral economics shows that humans are not purely self-interested. In addition, they strongly care aboutfairness. Therefore, multi-agent systems that fail to take fairness into account, may not be sufficiently aligned with human expectations and may not reach intended goals. In this paper, we present an overview of work in the area of fairness in multi-agent systems. More precisely, we first look at the classical agent model, that is, rational decision making. We then provide an outline of descriptive models of fairness, that is, models that explain how and why humans reach fair decisions. Then, we look at prescriptive, computational models for achieving fairness in adaptive multi-agent systems. We show that results obtained by these models are compatible with experimental and analytical results obtained in the field of behavioral economics.
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Furioli, Giulia, Ada Pulvirenti, Elide Terraneo, and Giuseppe Toscani. "Fokker–Planck equations in the modeling of socio-economic phenomena." Mathematical Models and Methods in Applied Sciences 27, no. 01 (January 2017): 115–58. http://dx.doi.org/10.1142/s0218202517400048.
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We present and discuss various one-dimensional linear Fokker–Planck-type equations that have been recently considered in connection with the study of interacting multi-agent systems. In general, these Fokker–Planck equations describe the evolution in time of some probability density of the population of agents, typically the distribution of the personal wealth or of the personal opinion, and are mostly obtained by linear or bilinear kinetic models of Boltzmann type via some limit procedure. The main feature of these equations is the presence of variable diffusion, drift coefficients and boundaries, which introduce new challenging mathematical problems in the study of their long-time behavior.