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

Pareto-optimality in linear public goods games

We derive a generalized method for calculating the total number of Paretooptimal allocations (NOPA) in typical linear public goods games. Among other things, the method allows researchers to develop new experimental designs for testing the relevance of Pareto-optimality in experimental settings, for investigating alternative causes of the decline of voluntary contributions, or for analyzing the contribution behavior of the rich and poor in heterogeneous income settings. Further findings include that the NOPA is related to the marginal per capita return (MPCR) of a contribution to the public good and that the maximum number of free-riders tolerated by the Paretooptimality concept is independent from the group size and income distribution. Finally, we apply our findings to a number of published linear public goods games, suggest an agenda for future research and provide a MATLAB code

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

Interactive effects of multiple stressors in coastal ecosystems

Coastal ecosystems are increasingly experiencing anthropogenic pressures such as climate heating, CO2 increase, metal and organic pollution, overfishing and resource extraction. Some resulting stressors are more direct like fisheries, others more indirect like ocean acidification, yet they jointly affect marine biota, communities and entire ecosystems. While single-stressor effects have been widely investigated, the interactive effects of multiple stressors on ecosystems are less researched. In this study, we review the literature on multiple stressors and their interactive effects in coastal environments across organisms. We classify the interactions into three categories: synergistic, additive, and antagonistic. We found phytoplankton and mollusks to be the most studied taxonomic groups. The stressor combinations of climate warming, ocean acidification, eutrophication, and metal pollution are the most critical for coastal ecosystems as they exacerbate adverse effects on physiological traits such as growth rate, basal respiration, and size. Phytoplankton appears to be most sensitive to interactions between metal and nutrient pollution. In nutrient-enriched environments, the presence of metals considerably affects the uptake of nutrients, and increases respiration costs and toxin production in phytoplankton. For mollusks, warming and low pH are the most lethal stressors. The combined effect of heat stress and ocean acidification leads to decreased growth rate, shell size, and acid-base regulation capacity in mollusks. However, for a holistic understanding of how coastal food webs will evolve with ongoing changes, we suggest more research on ecosystem-level responses. This can be achieved by combining in-situ observations from controlled environments (e.g. mesocosm experiments) with modelling approaches

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