Coastal ecosystems are increasingly experiencing anthropogenic pressures such as climate warming, CO2 increase, metal and organic pollution, overfishing, and resource extraction. Some resulting stressors are more direct like pollution and fisheries, and 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 bivalves to be the most studied taxonomic groups. Climate warming is identified as the most dominant stressor which, in combination, with other stressors such as ocean acidification, eutrophication, and metal pollution exacerbate adverse effects on physiological traits such as growth rate, fitness, basal respiration, and size. Phytoplankton appears to be most sensitive to interactions between warming, metal and nutrient pollution. In warm and nutrient-enriched environments, the presence of metals considerably affects the uptake of nutrients, and increases respiration costs and toxin production in phytoplankton. For bivalves, 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 bivalves. 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.

Viable North Sea (ViNoS) is an Agent-based Model (ABM) of the German Small-scale Fisheries.As a Social-Ecological Systems model it focusses on the adaptive behaviour of fishers facing regulatory, economic, and resource changes.Small-scale fisheries are an important part both of the cultural perception of the German North Sea coast and of its fishing industry.These fisheries are typically family-run operations that use smaller boats and bottom trawling gear to catch a variety of demersal species, foremost plaice, sole, and brown shrimp.

Abstract Allingham and Sandmo (J. Public Econ., 1972) analyze by the example of tax evasion and non-compliance the intended underreporting of taxpayers via concave and twice differentiable utility functions within Becker's economics-of-crime theory (J. Political Econ., 1968) on behavioral aspects of illicit activities. This work is concerned with how to build feasible utility functions applicable for experiments, theoretical investigations and / or numerical simulations of any kind of such illicit activities. It turns out that feasible utility functions form a set of Allingham-Sandmo-Functions applicable for Risk Averse and Neutral Taxpayers (ASFRANT) which is a non-commutative semiring with left-annihilating zero and unity. JEL classification numbers: C02, H26. Keywords: Agent-based Modeling, Expected Utility, Semiring, Tax Evasion.

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.

We investigate a heterogeneous Ising model in the context of tax evasion dynamics, where different types of agents are parameterised via local temperatures and magnetic fields. Our work focuses on the dynamic behavioural change of agents after an audit, which either corresponds to a temporal reduction or enhancement of compliance, also known under the terms of 'bomb crater effect' and 'target effect', respectively. We analyse this effect for different types of agents: endogenously non-compliant types; agents that have a tendency to copy (non-) compliant behaviour from their social environment; ethical agents with strong endogenous moral attitudes; and random types that show large fluctuations between compliant and noncompliant behaviour. Each type influences overall tax evasion differently and our model predicts that, interestingly, increasing the audit probability can have the counter-intuitive effect of increasing tax evasion under certain circumstances. We analyse audit strategies that can suppress this effect, and thus contribute to the burgeoning literature on the actual impact of tax audits.

There is an ongoing discussion concerning the relationship between social welfare and climate change, and thus the required level and type of measures needed to protect the climate. Integrated assessment models (IAMs) have been extended to incorporate technological progress, heterogeneity and uncertainty, making use of a (stochastic) dynamic equilibrium approach in order to derive a solution. According to the literature, the IAM class of models does not take all the relationships among economic, social and environmental factors into account. Moreover, it does not consider these interdependencies at the micro-level, meaning that all possible consequences are not duly examined. Here, we propose an agent-based approach to analyse the relationship between economic welfare and climate protection. In particular, our aim is to examine how the decisions of individual agents, allowing for the trade-off between economic welfare and climate protection, influence the aggregated emergent economic behaviour. Using this model, we estimate a damage function, with values in the order 3% - 4% for 2 degrees C temperature increase and having a linear (or slightly concave) shape. We show that the heterogeneity of the agents, technological progress and the damage function may lead to lower GDP growth rates and greater temperature-related damage than what is forecast by models with solely homogeneous (representative) agents.

Even if surface warming could be kept below 2.0°C or 1.5°C by 2100, global sea-level rise will occur for several centuries or even millennia. One possible interpretation of a successful climate policy for the next few decades could be that it should avoid global-warming induced impacts on climate, ecosystems and human societies not only within this century, but also for the next centuries and beyond. Here, we perform a proof-of-concept study to introduce a constraint on SLR as a new climate target and compare the economic impact to that of a corresponding temperature target. In the 21st yearly session of the Conference of the Parties in Paris in 2015, SLR threats to the Small Island Developing States (SIDS) prompted a commitment to strive for a lower global temperature target goal of limiting surface warming below 1.5°C. However, an SLR target more directly relates to their existential threats. We here substantially augmented the climate model of the optimizing climate-energy-economy model MIND (Model of Investment and Technological Development) from an impulse-response model to a three-layer ocean model with much-improved representation of ocean heat uptake. We introduce a global total SLR model with four components, one due to ocean thermal expansion, one due to Greenland ice-sheet melting, one due to Antarctic ice-sheet melting, and one due to mountain glaciers and ice cap melting. The newly developed integrated-assessment framework has enabled us to investigate, for the first time, a sea-level rise climate target. Our results emphasize a key effect of carbon emissions pathways on the future SLR after the 21st century. The shape of carbon emissions pathways will strongly influence future SLR after the 21st century and generally affect SIDS over centuries. To reduce SLR-induced impacts on SIDS, a target is required that not only keeps surface warming below a certain level but also reduces surface warming substantially thereafter. We find that a global SLR target will provide a more sustainable and a lower-cost solution to limit both short-term and long-term climate changes for stakeholders who primarily care about SLR among all global warming impact categories compared to a temperature target with the same SLR by 2200. We find that the SLR target can provide a temperature overshoot profile through a physical constraint rather than arbitrarily defining an overshoot range of temperature as acceptable. Temperature targets with a limited overshoot have been invoked to make the 2.0° and 1.5°C targets feasible in the context of real-world United Nations climate policy; however, rational constraints on the temperature overshoot have been unclear. SLR targets can be viewed as a reinterpretation of the 2.0° and 1.5°C targets and can provide a rational justification of a certain temperature overshoot for stakeholders who primarily care about SLR. Our present framework with reinterpretation of the widely agreed temperature targets can, in principle, be transferred from SLR targets to impact-related climate targets and can be used to identify a more sustainable path toward meeting the Paris Agreement.

In professional sports, the amounts disbursed in rank-based prize money distributions decline sharply, and differences in performance are extremely small. This disparity may provide a high incentive for doping. Due to the complexity of doping, obtaining meaningful insights on the influence of prize money distribution and the pecuniary value of prize money on doping behaviour of elite athletes using game theory or other approaches has not been possible. The authors perform a computerised social simulation through agent-based modelling to analyse doping behaviour in competitive sport. The results show that the distribution of prize money in particular has an enormous impact on the prevalence of doping. By contrast, the total amount of prize money is less decisive for doping behaviour. Further, doping costs are observed to have only a marginal effect on doping prevalence, depending on the tested prize money distribution and its amount. The simulation results can be used by sports federations and competition organisers who should distribute the prize money more evenly to all athletes to reduce doping. (C) 2019 Sport Management Association of Australia and New Zealand. Published by Elsevier Ltd. All rights reserved.

The global temperature targets of limiting surface warming to below 2.0°C or even to 1.5°C have been widely accepted through the Paris Agreement. However, limiting surface warming has previously been proven insufficient to control sea level rise (SLR). Here, we explore a sea level target that is closer to coastal planning and associated adaptation measures than a temperature target. We find that a sea level target provides an optimal temperature overshoot profile through a physical constraint of SLR. The allowable temperature overshoot leads to lower mitigation costs and more effective long-term sea level stabilization compared to a temperature target leading to the same SLR by 2200. With the same mitigation cost as the temperature target, a SLR target could bring surface warming back to the targeted temperatures within this century, lead to a reduction of surface warming of the next century, and reduce and slow down SLR in the centuries thereafter.

The goal of environmental exposure modelling is to link fundamental human activities with stress via the environment. Stress is here defined as environmental conditions negatively affecting human health and well-being. Especially in urban areas, humans can be exposed to multiple stressors such as air pollution, noise (e.g. traffic), and heat. The importance of being able to predict the exposure level in urban areas is increasing due to ongoing urbanization and global climate change. For instance, in Germany annual Greenhouse Gas (GHG) emissions have been reduced by 28% from 1990 to 2014 but contributions by the transport sector have been quite stable (from 0.163 GtCO2Equivalents in 1990 to 0.160 GtCO2Equivalents in 2014 (Umweltbundesamt, 2016). Yang et al. (2018) provides a stylized agent-based model of human exposure to environmental stressors (heat, rain, NO2) for Hamburg, Germany. Within this ABM, the changing exposure to environmental stressors is analyzed for citizens as a function of time and location. The population is classified into different archetypes; they range from young, single students to families with children to old, rich and single persons. While their choice of transportation is a function of exposure, commuting time and costs, each agent has different preferences and different rates to adapt to changing environmental conditions. The agents are moving in multiple layers of housing (e.g. residential buildings) and infrastructure (e.g. streets, subway). Depending on the agent types, bike, car or public transport is chosen as the preferred mean of transport. However, Yang et al. (2018) consider stylized agent-based dynamics without any interaction among the agents. We provide a multi-agent docking study of human exposure to environmental stressors implemented in Netlogo and find distributional and relational equivalence (Axtell et al., 1996, Hokamp et al. 2018) to Yang et al. (2018). To put it differently, we analyze interacting individual heterogeneous agents in an actual urban environment. Results give information about the mean of transportation with the lowest exposure and how very low costs for public transport affect choices of transportation and so the road traffic. Further, the results may be used by policy makers and citizens (e.g. via mobile devices using an app) to improve environmental quality of life. References Axtell, R., Axelrod, R., Epstein, J.M., and Cohen, M.D. (1996) Aligning simulation models: a case study and results. Computational & Mathematical Organization Theory, 1 (2), 123–141. Hokamp, S., Gulyas, L., Koehler, M. and Wijesinghe, S. (2018), Agent-based Modelling and Tax Evasion: Theory and Application, 3-35, Hoboken, NJ, John Wiley & Sons Ltd. Umweltbundesamt (2014) Submission under the United Nations Framework Convention on Climate Change and the Kyoto Protocol 2016 – National Inventory Report for the German Greenhouse Gas Inventory 1990-2014. Yang, L. E., Hoffmann, P., Scheffran, J. , Rühe, S. , Fischereit, J. and Gasser, I. (2018), An Agent-Based Modeling Framework for Simulating Human Exposure to Environmental Stresses in Urban Areas, Urban Science, 2, 36.

While the formal study of tax evasion began with a seminal paper by M.G. Allingham and A. Sandmo titled “Income Tax Evasion: A Theoretical Analysis” in 1972, scholars and practitioners continue to be challenged with designing and implementing policies and incentives to mitigate tax evasion. While early theoretical studies provide a baseline from which to evaluate hypotheses, the methodology underlying the classical formulation; that is, the use of utility functions and the assumptions of taxpayer homogeneity and rationality, fall short in characterizing taxpayer behaviors observed in practice. In this book, we seek to advance the state of the art in the study of tax evasion by presenting an alternative computational approach based on simulating individual agents. These so-called agent-based models (ABM) aim to take into account individual preferences and can accommodate a larger variety of intrinsic and extrinsic variables to help explore a broader space of compliance outcomes. In this introductory chapter, we present a formal definition of tax evasion in Section 1.2 and outline the case for why its analysis is a priority not only for tax administrators, but also for society at large. The classical theoretical models of tax evasion are then summarized

Chapter 9 Agent-Based Simulations of Tax Evasion: Dynamics by Lapse of Time, Social Norms, Age Heterogeneity, Subjective Audit Probability, Public Goods Provision, and Pareto-Optimality Sascha Hokamp, Sascha HokampSearch for more papers by this authorAndrés M. Cuervo Díaz, Andrés M. Cuervo DíazSearch for more papers by this author Sascha Hokamp, Sascha HokampSearch for more papers by this authorAndrés M. Cuervo Díaz, Andrés M. Cuervo DíazSearch for more papers by this author Book Editor(s):Sascha Hokamp, Sascha Hokamp Universität HamburgSearch for more papers by this authorLászló Gulyás, László Gulyás Eötvös Loránd UniversitySearch for more papers by this authorMatthew Koehler, Matthew Koehler The MITRE CorporationSearch for more papers by this authorSanith Wijesinghe, Sanith Wijesinghe The MITRE CorporationSearch for more papers by this author First published: 27 February 2018 https://doi.org/10.1002/9781119155713.ch9 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onFacebookTwitterLinked InRedditWechat Summary Dynamics by lapse of time, social norm updating, age heterogeneity, subjective audit probability, public goods provision, and Pareto-optimality in heterogeneous populations are analyzed with the aid of an agent-based tax evasion model. We assume the taxpayers to pursue heterogeneity in tax declaration behavior due to neoclassical expected utility maximization, social interaction, ethical motivation, and erratic perception. We reaffirm the conclusion drawn elsewhere in the literature that the dynamics by lapse of time and social norm updating, in particular, strongly affect the extent of tax evasion at the macro level and we add a new explanation at the behavioral group level. Our calibration with experimental data from a tax declaration game and our sensitivity analysis provide novel insights into how strongly parameters influence tax compliance at the macro level as well as the behavioral group level; in line with the literature the penalty and tax rate significantly affect the extent of tax evasion negatively and positively, respectively. Agent-based Modeling of Tax Evasion: Theoretical Aspects and Computational Simulations RelatedInformation

We 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's 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.

2015-111 11:25 AM (Joliet) As long as competitive professional sports exists the phenomenon of using illicit methods like doping does maintain. Because of a seemingly endless series of scandals doping gains increasingly public attention. The official Anti-Doping Testing Figures, published annually by the World Anti-Doping Agency (WADA), show that the rate of Adverse Analytical Findings (positive Tests) is approximately 2% (WADA, 2014). But banned substances and methods may not be detectable and encompassing doping controls may not be feasible, because of causing enormous costs and organizational efforts. Therefore, we believe that the official figures underestimate the true extent of doping. Recent research activities in this field are based on various methods to approximate an extent of doping, but estimates differ essentially. To begin with, e.g. Sottas et al. (2011) make use of a forensic approach and analyze 7,289 blood samples collected from 2,737 athletes. They detected abnormal blood profiles and figure out that approximately 14% of the tested athletes commit blood doping. Other authors try to estimate the extent of doping by using self-reports. To ensure that the athlete’s answers are anonymous, applying randomized response technique is a common practice to carry out such surveys. Although very similar methods are applied, the results vary a lot. While Pitsch, Emirch, and Klein (2007) and Breuer and Hallmann (2013) find about 5-7% as extent of doping, Plessner and Musch (2002) estimate that more than 34% of the athletes are doped and Striegel, Ulrich, and Simon (2010) present a lower interval limit of 25% and an upper limit of 48%. Looking at the results of projections methods, the estimated extent of doping varies even more and goes from 6% (Waddington, Malcolm, Roderick, & Naik, 2005) up to 72% (Anshel, 1991). To sum up, we find in the literature estimations for extents of doping at a range of 2% to 72%. Further investigations also differ in extents of doping estimated (e.g. James, Nepusz, Naughton, & Petro ́czi, 2013; Petróczi, Mazanov, Nepusz, Backhouse, & Naughton, 2008; Uvacsek et al., 2011) which supports our notion of a complexity problem to identify a real extend of doping. To elude this complexity problem in professional sports researchers develop various game theory models based on rational choice theory like strategic games (e.g. Berentsen, 2002; Berentsen & Lengwiler, 2004; Breivik, 1987; Haugen, 2004; Eber, 2008; Eber & J. The ́pot, 1999; Haugen, Nepusz, & Petro ́czi, 2013; Kra ̈kel, 2007; Ryvkin, 2013) and inspection games (e.g. Berentsen, Bru ̈gger, & Loertscher, 2008; Buechel, Emrich, & Pohlkamp, 2013; Haugen, 2004; Kirstein, 2012; Maennig, 2002). A common feature of these models is to depict doping behavior patterns in professional sports. But we think that these models exhibit a low degree of complexity, because of being analytically solvable. Since we recognize a lack of reliable empirical data computer simulations permit to explore and elucidate doping behavior patterns in professional sports (Petróczi & Aidman, 2008). In line with the literature, we think that agentbased modeling has potential as a ‘third way’ of doing social science in addition to argumentation and formalization (Gilbert & Terna, 2000). Making use of agent-based modeling, we are able to formalize theories on complex social processes like doping behavior patterns in professional sports. Thus, modeling a high degree of complexity is an essential advantage of an agent-based approach compared to game theory models. Our multi-period agent-based doping concept is based on three interacting objectives which are (i) elite-athletes, (ii) anti-doping laboratory and (iii) anti-doping agency. The latter agency announces anti-doping rules and imposes fines as well as bans. Anti-doping laboratory executes doping controls whereby control frequency and efficiency are imperfect so that not any doped and tested elite-athlete is detected as a doping sinner. Each time period any eliteathlete competes for income in a rank-order tournament. We assume that usage of doping increases elite-athlete’s 2015 North American Society for Sport Management Conference (NASSM 2015) Ottawa, ON June 2 – 6, 2015 Page 401 chance of success in the rank-order tournament in the short term but such an illegal practice causes an adverse reaction in the long term. In particular, we consider four agent types that are (a) rational, (b) suggestible, (c) compliant and (d) erratic. Rational sportsmen may use doping substances with respect to an expected utility maximizing approach. A suggestible athlete takes into account the doping behavior committed in his social network. A compliant agent accepts and follows the rules of the anti-doping agency. An erratic player wants to act rulecompliant but may commit doping unintentional. Using the agent-based modeling approach combined with a sensitivity analysis, we aim at testing how parameters like bans, fines, test efficiency, testing frequency, prize-money distributions and subjective detection probabilities may influence elite-athletes doping behavior. On the basis of the simulation results, policy recommendations for the fight against doping like optimal budget allocation for different prevention policies can be given.

In an economically globalized world, new and hitherto hardly satisfactory mastered governance tasks arise, in particular for the taxation of companies and capital.The example of Greece, Italy, and Spain shows what kind of problems for public finance may occur when a significant proportion of economic activity takes place in the shadows, i.e., without paying duties and taxes.This will not only undermine tax morale in general, but also complicates the provision of public goods as, e.g., roads and bridges, or increases national debt.As the sovereign debt crisis in the euro zone demonstrated, a large shadow economy can have significant negative economic consequences for any member state in a currency union.The shadow economy and tax evasion are at least partially caused by misguided tax policies, as well as by other weaknesses of local and federal governance.Without a deeper understanding of the shadow economy and tax evasion, as well as their relation to tax policy and state governance, an effective and efficient reduction of these side effects of government policy is nearly impossible.To study determinants and consequences of the shadow economy and tax evasion, the biannual Münster shadow economy conferences have been established in 2009.The conferences are dedicated to the empirical, experimental, agent-based and theoretical analysis of all topics surrounding the shadow economy, tax evasion and tax compliance.The scientific disciplines involved cover economics, econophysics, and psychology, to name only a few.The participants come from a large number of countries, from South Korea to the United States and from Russia to South Africa.