Income tax evasion in a society of heterogeneous agents: Evidence from an agent-based model

We analyze the evolution and extent of income tax evasion under alternative governmental policies in an agent-based model with heterogeneous agents. A novel aspect of our modeling is the use of an exponential utility function, which allows us to assume rather realistic audit probabilities and to yield more realistic results with respect to the extent of tax evasion. Further, the introduction of lapse of time effects constitutes another novel aspect of our model. Among other things, the model allows for assessing the impact of alternative policies on tax evasion. Subject to the model features, we find that ethical norms and lapse of time effects reduce the extent of tax evasion particularly strong. --income tax evasion,heterogeneous population,lapse of time,ethical behavior,agent-based models

Exakte Bestimmung der Stabilitätsbedingungen von Marktprozessen auf der Basis des Koordinationsmängel-Diagnosekonzepts und des Schur-Theorems

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An agent-based concept to analyze elite-athletes' doping behavior

A seemingly endless series of scandals has focused increasing public attention on the issue of doping among elite athletes. But we still do not know how many elite athletes really make use of banned drugs. In addition, we recognize the literature suffers a lack of appropriate game theory models for complex social interactions related to doping. Therefore, we think that an agent-based approach may allow doping behavior patterns in professional sports to be explored and elucidated. We conceptualize an agent-based model on three interacting objectives, namely (i) elite athletes, (ii) anti-doping laboratory and (iii) anti-doping agency. The latter agency announces antidoping rules and imposes penalties; the anti-doping laboratory executes doping controls and elite athletes compete for income. In particular, we focus on presenting an agent-based concept to analyze elite athletes' doping behavior. Using such an agentbased framework and computational simulations may lead in the future to policy recommendations for the fight against doping

Advances in Computational Social Science and Social Simulation

Aquesta conferència és la celebració conjunta de la "10th Artificial Economics Conference AE", la "10th Conference of the European Social Simulation Association ESSA" i la "1st Simulating the Past to Understand Human History SPUHH".Conferència organitzada pel Laboratory for Socio­-Historical Dynamics Simulation (LSDS-­UAB) de la Universitat Autònoma de Barcelona.Readers will find results of recent research on computational social science and social simulation economics, management, sociology,and history written by leading experts in the field. SOCIAL SIMULATION (former ESSA) conferences constitute annual events which serve as an international platform for the exchange of ideas and discussion of cutting edge research in the field of social simulations, both from the theoretical as well as applied perspective, and the 2014 edition benefits from the cross-fertilization of three different research communities into one single event. The volume consists of 122 articles, corresponding to most of the contributions to the conferences, in three different formats: short abstracts (presentation of work-in-progress research), posters (presentation of models and results), and full papers (presentation of social simulation research including results and discussion). The compilation is completed with indexing lists to help finding articles by title, author and thematic content. We are convinced that this book will serve interested readers as a useful compendium which presents in a nutshell the most recent advances at the frontiers of computational social sciences and social simulation researc

Decarbonizing the Global Economy—Investigating the Role of Carbon Emission Inertia Using the Integrated Assessment Model MIND

In 2015, the 21st Conference of the Parties reaffirmed the target of keeping the global mean temperature rise below 2 °C or 1.5 °C by 2100 while finding no consensus on how to decarbonize the global economy. In this regard, the speed of decarbonization reflects the (in)flexibility of transforming the energy sector due to engineering, political, or societal constraints. Using economy–energy–climate-integrated assessment models (IAMs), the maximum absolute rate of change in carbon emission allowed from each time step to the next, so-called carbon emission inertia (CEI), governs the magnitude of emission change, affecting investment decisions and economic welfare. Employing the model of investment and endogenous technological development (MIND), we conduct a cost-effectiveness analysis and examine anthropogenic global carbon emission scenarios in line with decarbonizing the global economy while measuring the global mean temperature. We examine the role of CEI as a crucial assumption, where the CEI can vary in four scenarios from 3.7% to 12.6% p.a. We provide what-if studies on global carbon emissions, global mean temperature change, and investments in renewable energy production and show that decarbonizing the global economy might still be possible before 2100 only if the CEI is high enough. In addition, we show that climate policy scenarios with early decarbonization and without negative emissions may still comply with the 2 °C target. However, our results indicate that the 1.5 °C target is not likely to be reached without negative emission technologies. Hence, the window of opportunity is beginning to close. This work can also assist to better interpret existing publications on various climate targets when altering CEI could have played a significant role

An agent-based concept to analyze elite-athletes' doping behavior

A seemingly endless series of scandals has focused increasing public attention on the issue of doping among elite athletes. But we still do not know how many elite athletes really make use of banned drugs. In addition, we recognize the literature suffers a lack of appropriate game theory models for complex social interactions related to doping. Therefore, we think that an agent-based approach may allow doping behavior patterns in professional sports to be explored and elucidated. We conceptualize an agent-based model on three interacting objectives, namely (i) elite athletes, (ii) anti-doping laboratory and (iii) anti-doping agency. The latter agency announces antidoping rules and imposes penalties; the anti-doping laboratory executes doping controls and elite athletes compete for income. In particular, we focus on presenting an agent-based concept to analyze elite athletes' doping behavior. Using such an agentbased framework and computational simulations may lead in the future to policy recommendations for the fight against doping

Behavioural economics and tax evasion: calibrating an agent-based econophysics model with experimental tax compliance data

International audienceWe observe in the literature a persistent lack of calibrating agent - based econophysics tax evasion models. However, calibrations are indispensable to the quantitative and predictive application of such computational simulation approaches. Therefore, we analyse individual data from two tax compliance experiments with social interaction: from information on tax enforcement measures in groups with income heterogeneity, where the audit probability is known and audit results are publicly and officially announced; and from information about the mean reported income of other group members in the previous period. In our agent - based econophysics simulation, we implement recent advances in behavioural economics, for instance to describe social interactions within a population of behaviourally heterogeneous taxpayers. For this purpose, we employ experimental data showing a bimodal distribution which allows us to apply Ising\textquoterights description of magnetism, a model adopted from statistical physics that can be related to binary choice models. We restrict agents in our econophysics framework to show selfish, imitating, ethical or random motives in their decisions to declare income. We find that the subjects in the experimental laboratory pursue rather mixed behaviour, including random and imitating motives

Behavioural economics and tax evasion: calibrating an agent-based econophysics model with experimental tax compliance data

International audienceWe observe in the literature a persistent lack of calibrating agent - based econophysics tax evasion models. However, calibrations are indispensable to the quantitative and predictive application of such computational simulation approaches. Therefore, we analyse individual data from two tax compliance experiments with social interaction: from information on tax enforcement measures in groups with income heterogeneity, where the audit probability is known and audit results are publicly and officially announced; and from information about the mean reported income of other group members in the previous period. In our agent - based econophysics simulation, we implement recent advances in behavioural economics, for instance to describe social interactions within a population of behaviourally heterogeneous taxpayers. For this purpose, we employ experimental data showing a bimodal distribution which allows us to apply Ising\textquoterights description of magnetism, a model adopted from statistical physics that can be related to binary choice models. We restrict agents in our econophysics framework to show selfish, imitating, ethical or random motives in their decisions to declare income. We find that the subjects in the experimental laboratory pursue rather mixed behaviour, including random and imitating motives