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Modeling the corrupt tendencies of cooperating individuals with losing tolerance
Abstract
Cooperation among humans is necessary to accomplish tasks that are difficult to do alone. This study explores one of its negative effects, commonly known as corruption, by adopting a die-rolling game that has been previously reported to capture the emergence of corrupt tendencies of players. Here, all-male and all-female pairs play 20 rounds of the game where they gain points if they report the same number, referred to as a double. The total points accumulated by a pair earn them a reward according to a set matrix. Regardless of sex, the players reported a significantly higher number of doubles than expected assuming complete honesty. An agent-based model is created following the rules of the game and is modified to explore the effects of the ratio of honest players when players have a losing tolerance. We find that every time a player gains a chance to cheat after losing one round, the number of reported doubles is inflated. An initially honest population becomes a cheating population such that in the presence of losing tolerance, a player can never be completely honest in the entire duration of the game. Finally, the differences between the reported die-values provide an insight that the behavior of the first player may motivate the corrupt behavior of the second player in a pair.
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Corruption has fierce impacts on economic and societal development and is subject to a vast range of institutional, jurisdictional, societal, and economic conditions. It is this paper's aim to provide a reassessment and a comprehensive state-of-the-art survey of existing literature on corruption and its causes and effects. A particularly strong focus is put on presenting and discussing insights resulting from empirical research and contrasting recent with older findings.
We argue that cooperation is instinctual. Human cooperation conferred advantages to individuals in the ancestral environment in which evolution occurred. Explanations of the evolution of cooperation for any species (human, pre-human, and non-human) have to be consistent with the biological, physiological, and environmental constraints that existed in the ancestral environment during which evolutionary selection occurred. Our explanation is consistent with: (1) the anatomical evolution of humanity; (2) the paleontological and chronological evidence; and (3) modern biology.
Objective: To examine the relationship between loss-chasing, the propensity to continue gambling to recover from losses, alexithymia, a personality trait associated poor emotional processing and impulsivity, the tendency to act quickly without reflection or consideration of the consequences.
Method: Two experiments are reported (E1: N = 60, Males, 11; Age, 21.6 years. E2: N = 49, Males, 22; Age, 21.1 years). In experiment 1, two groups (low alexithymia, high alexithymia) completed the Cambridge Gambling Task (CGT). Loss-chasing behavior was investigated. In experiment 2, both alexithymia (low, high) and impulsivity (low, high) were examined also using the CGT. A further change was the order of bet proportion from ascending to descending.
Results: Experiment 1 shows loss-chasing behavior in participants high in alexithymia but not those low in alexithymia (ηp2=0.09). Experiment 2 shows loss-chasing behavior in participants both low and high in alexithymia but it was greater for participants high in alexithymia (ηp2 = 0.09). The effect of impulsivity was not statistically significant (ηp2 = 0.01). Loss-chasing behavior was correlated with the emotional facets of alexithymia but not the cognitive facet.
Conclusions: Alexithymia is a precursor to loss-chasing when gambling and loss-chasing reflects the cognitive and emotional aspects of gambling. Specifically, the tendency to loss-chase depends on the need to recoup previous losses and failure to process the emotional consequences of those losses.
Agent-based modeling (ABM) has become an increasingly important tool in computational science. Thus, in the final week of the 2013 fall semester, Wofford College's undergraduate Modeling and Simulation for the Sciences course (COSC/MATH 201) considered ABM using the NetLogo tool. The students explored existing ABMs and completed two tutorials that developed models on unconstrained growth and the average distance covered by a random walker. The models demonstrated some of the utility of ABM and helped illustrate the similarities and differences between agent-based modeling and previously discussed techniques— system dynamics modeling, empirical modeling, and cellular automaton simulations. Improved test scores and questionnaire results support the success of the goals for the week.
In recent years a growing body of empirical investigations has examined the causes and consequences of corruption. These investigations are mostly cross–country analyses, based on comparative assessments of the extent of corruption in various countries. Such assessments are sometimes compiled by private agencies to determine country risks, and the data gathered are sold to investors. Other sources are surveys of the general public or elite businesspeople. The data on corruption are thus based on sub¬jective perceptions and expertise, and empirical work using these indices assumes that they are correlated with underlying real levels of corruption. With the exception of some micro-level studies, perceptions data are the only information available on corruption levels. Efforts to use objective data, such as convictions for corruption or abuse of office, suffer from inherent biases that undermine their validity and are not available across a large cross-section of countries. Recognizing the limitations of perception-based indices, researchers have nevertheless been able to use them to significantly advance the study of corruption. These data allow empirical research on corruption to move beyond the anecdotal descriptions and purely theoretical considerations that previously dominated. This chapter reviews these studies and summarizes what we have learned.
Agent-based modeling (ABM) is an emerging modeling approach. In the past two decades, agent-based models have been increasingly adapted by social scientists, especially scientists in urban and geospatial studies, as an effective paradigm for framing the underlying problems of complex and dynamic processes. As a result, the literature of ABM research is growing rapidly, covering a diverse range of topics. This paper presents a systematic literature review of ABM research, and discusses both theoretical issues such as ABM definition and architecture, and practical issues such as ABM applications and development platforms. A comprehensive and up-to-date bibliography is presented.
Self-serving, rational agents sometimes cooperate to their mutual benefit. However, when and why cooperation emerges is surprisingly hard to pin down. To address this question, scientists from diverse disciplines have used the Prisoner’s Dilemma, a simple two-player game, as a model problem. In PNAS, Press and Dyson (1) dramatically expand our understanding of this classic game by uncovering strategies that provide a unilateral advantage to sentient players pitted against unwitting opponents. By exposing these results, Press and Dyson have fundamentally changed the viewpoint on the Prisoner’s Dilemma, opening a range of new possibilities for the study of cooperation.
Why do humans, uniquely among animals, cooperate in large numbers to advance projects for the common good. Contrary to the conventional wisdom in biology and economics, this generous and civic-minded behavior is widespread and cannot be explained simply by far-sighted self-interest or a desire to help close genealogical kin. InA Cooperative Species, Samuel Bowles and Herbert Gintis--pioneers in the new experimental and evolutionary science of human behavior--show that the central issue is not why selfish people act generously, but instead how genetic and cultural evolution has produced a species in which substantial numbers make sacrifices to uphold ethical norms and to help even total strangers.The authors describe how, for thousands of generations, cooperation with fellow group members has been essential to survival. Groups that created institutions to protect the civic-minded from exploitation by the selfish flourished and prevailed in conflicts with less cooperative groups. Key to this process was the evolution of social emotions such as shame and guilt, and our capacity to internalize social norms so that acting ethically became a personal goal rather than simply a prudent way to avoid punishment.Using experimental, archaeological, genetic, and ethnographic data to calibrate models of the coevolution of genes and culture as well as prehistoric warfare and other forms of group competition,A Cooperative Speciesprovides a compelling and novel account of how humans came to be moral and cooperative.
This paper provides an overview of research and development activities in the field of autonomous agents and multi-agent systems. It aims to identify key concepts and applications, and to indicate how they relate to one-another. Some historical context to the field of agent-based computing is given, and contemporary research directions are presented. Finally, a range of open issues and future challenges are highlighted.
Complex systems research is becoming ever more important in both the natural and social sciences. It is commonly implied that there is such a thing as a complex system across the disciplines. However, there is no concise definition of a complex system, let alone a definition that all disciplines agree on. We review various attempts to characterize a complex system, and consider a core set of features that are widely associated with complex systems by scientists in the field. We argue that some of these features are neither necessary nor sufficient for complexity, and that some of them are too vague or confused to be of any analytical use. In order to bring mathematical rigour to the issue we then review some standard measures of complexity from the scientific literature, and offer a taxonomy for them, before arguing that the one that best captures the qualitative notion of complexity is that of the statistical complexity. Finally, we offer our own list of necessary conditions as a characterization of complexity. These conditions are qualitative and may not be jointly sufficient for complexity. We close with some suggestions for future work.
The scale of human cooperation is an evolutionary puzzle. All of the available evidence suggests that the societies of our Pliocene ancestors were like those of other social primates, and this means that human psychology has changed in ways that support larger, more cooperative societies that characterize modern humans. In this paper, we argue that cultural adaptation is a key factor in these changes. Over the last million years or so, people evolved the ability to learn from each other, creating the possibility of cumulative, cultural evolution. Rapid cultural adaptation also leads to persistent differences between local social groups, and then competition between groups leads to the spread of behaviours that enhance their competitive ability. Then, in such culturally evolved cooperative social environments, natural selection within groups favoured genes that gave rise to new, more pro-social motives. Moral systems enforced by systems of sanctions and rewards increased the reproductive success of individuals who functioned well in such environments, and this in turn led to the evolution of other regarding motives like empathy and social emotions like shame.
Corruption in the public sector erodes tax compliance and leads to higher tax evasion. Moreover, corrupt public officials abuse their public power to extort bribes from the private agents. In both types of interaction with the public sector, the private agents are bound to face uncertainty with respect to their disposable incomes. To analyse effects of this uncertainty, a stochastic dynamic growth model with the public sector is examined. It is shown that deterministic excessive red tape and corruption deteriorate the growth potential through income redistribution and public sector inefficiencies. Most importantly, it is demonstrated that the increase in corruption via higher uncertainty exerts adverse effects on capital accumulation, thus leading to lower growth rates.
Why do humans, uniquely among animals, cooperate in large numbers to advance projects for the common good? Contrary to the conventional wisdom in biology and economics, this generous and civic-minded behavior is widespread and cannot be explained simply by far-sighted self-interest or a desire to help close genealogical kin. This book shows that the central issue is not why selfish people act generously, but instead how genetic and cultural evolution has produced a species in which substantial numbers make sacrifices to uphold ethical norms and to help even total strangers. The book describes how, for thousands of generations, cooperation with fellow group members has been essential to survival. Groups that created institutions to protect the civic-minded from exploitation by the selfish flourished and prevailed in conflicts with less cooperative groups. Key to this process was the evolution of social emotions such as shame and guilt, and our capacity to internalize social norms so that acting ethically became a personal goal rather than simply a prudent way to avoid punishment. Using experimental, archaeological, genetic, and ethnographic data to calibrate models of the coevolution of genes and culture as well as prehistoric warfare and other forms of group competition, the book provides a compelling and novel account of human cooperation.
Self-organization of cooperative behavior has been particularly interesting especially in communities where selfishness dominates more than social cooperativeness. These situations have often been described by the spatial prisoner's dilemma game. In reality, however, a centralized control is often necessary to establish social order. But the robustness of this established social cooperation remains unclear. Using the spatial prisoner's dilemma, we explore the evolution of cooperation not only in a community governed by central control, but also in selfish and realistic (law-abiding but selfish individuals) communities, by introducing two sources of stochasticities: defection-driven noise and stochastic payoff mutations. Individuals could imitate their best-performing neighbors, migrate to better locations, or do both. We show that although the presence of a central control is able to drive and maintain cooperation that is robust even in the presence of noise, social cooperation instantly collapse in the presence of defection-driven noise when migration alone is the only viable option. We also show that stochastic payoff variations enhance cooperation in realistic communities, while selfish communities are highly susceptible to defection-driven noise and stochastic payoff mutations.
This paper makes use of recent empirical results, mainly from experimental economics, to expore the conditions under which people will cooperate and to assess competing explantions of this cooperation. It is argued that the evidence supports the claim that people differ in type, with some being conditional cooperators and others being motivated by more or less sophisticated forms of self-interest. Stable cooperation requires, among other things, rules and institutions that protect conditional cooperators from myopically self-interested types. Additional empirical features of the behavior of conditional cooperators also imply that rules and institutions are required to produce stable cooperation.
Many Internet services depend on the integrity of their users, even when these users have strong incentives to behave dishonestly. Drawing on experiments in two different online contexts, this study measures the prevalence of cheating and evaluates two different methods for deterring it. Our first experiment investigates cheating behavior in a pair of online exams spanning 632 students in India. Our second experiment examines dishonest behavior on Mechanical Turk through an online task with 2,378 total participants. Using direct measurements that are not dependent on self-reports, we detect significant rates of cheating in both environments. We confirm that honor codes-despite frequent use in massive open online courses (MOOCs)-lead to only a small and insignificant reduction in online cheating behaviors. To overcome these challenges, we propose a new intervention: a stern warning that spells out the potential consequences of cheating. We show that the warning leads to a significant (about twofold) reduction in cheating, consistent across experiments. We also characterize the demographic correlates of cheating on Mechanical Turk. Our findings advance the understanding of cheating in online environments, and suggest that replacing traditional honor codes with warnings could be a simple and effective way to deter cheating in online courses and online labor marketplaces.
Game theory is the theory of strategic thinking. Developed for military purposes and defense, in the past it has also been used as an alternative and complementary approach to deal with robustness in the presence of worst-case uncertainties or disturbances in many areas such as economics, engineering, computer science, just to name a few. However, game theory is recently gaining ground in systems and control engineering, mostly in engineered systems involving humans, where there is a trend to use game theoretic tools to design protocols that will provide incentives for people to cooperate. For instance, scientists tend to use game theoretic tools to design optimal traffic flows, or predicting or avoiding blackouts in power networks or congestion in cyber-physical networked controlled systems. Incentives to cooperate are also crucial in dynamic resource allocation, multi-agent systems and social models (including social and economic networks). This paper assembles the material of two graduate courses given at the Department of Engineering of the University of Oxford in June-July 2013 and at the Department of Electrical and Electronic Engineering of Imperial College, in October-December 2013. The paper covers the foundations of the theory of noncooperative and cooperative games, both static and dynamic. It also highlights new trends in cooperative differential games, learning, approachability (games with vector payoffs) and mean-field games (large number of homogeneous players). The course emphasizes theoretical foundations, mathematical tools, modeling, and equilibrium notions in different environments.
Significance
Recent financial scandals highlight the devastating consequences of corruption. While much is known about individual immoral behavior, little is known about the collaborative roots of curruption. In a novel experimental paradigm, people could adhere to one of two competing moral norms: collaborate vs. be honest. Whereas collaborative settings may boost honesty due to increased observability, accountability, and reluctance to force others to become accomplices, we show that collaboration, particularly on equal terms, is inductive to the emergence of corruption. When partners' profits are not aligned, or when individuals complete a comparable task alone, corruption levels drop. These findings reveal a dark side of collaboration, suggesting that human cooperative tendencies, and not merely greed, take part in shaping corruption.
The aim of this study was to investigate how the Behavioral Approach System (BAS) and the Behavioral Inhibition System (BIS) affect decision-making in a gambling task. In accordance with the joint subsystem hypothesis, participants were divided into four groups based on their BAS and BIS scores. We used a modified gambling task, which examines decision-making after having winning and losing experiences unknowingly manipulated by the experimenters. We found that the high BAS and low BIS group made the most risky decisions after a winning experience, while the low BAS and high BIS group made more non-risky decisions after a losing experience. On the irrational belief scale, the high BAS groups bet larger amounts and had higher confidence levels in a losing condition. The present study found that relationships between personality traits and winning probabilities influence decisions in the gambling task. These findings may provide evidence that decision making and chasing in gambling situations are affected by personality traits and a perspective on feedback types.
This study reports and analyzes the results of the second round of the computer tournament for the iterated Prisoner's Dilemma. The object is to gain a deeper understanding of how to perform well in such a setting. The 62 entrants were able to draw lessons from the results of the first round and were able to design their entries to take these lessons into account. The results of the second round demonstrate a number of subtle pitfalls which specific types of decision rules can encounter. The winning rule was once again TIT FOR TAT, the rule which cooperates on the first move and then does what the other player did on the previous move. The analysis of the results shows the value of not being the first to defect, of being somewhat forgiving, but also the importance of being provocable. An analysis of hypothetical alternative tournaments demonstrates the robustness of the results.
This paper deals with the problem of supervising and monitoring a photovoltaic (PV) plant. First, an offline descriptive and inferential statistical procedure for evaluating the goodness of system performance is presented. Then, an online inferential algorithm for real-time monitoring and fault detection is introduced. The two methodologies utilize the energy output of inverters as input data and are valid for both Gaussian and non-normal distribution of data. The procedures have been tested on a real PV installation, and results are reported for the case of a grid-connected PV plant in Italy for which one PV module over 132 resulted in being badly connected.
The phenomenon of `chasing' has been identified as one of the central characteristics of the behavior of pathological gamblers [American Psychiatric Association (1994). Diagnostic and statistical manual of mental disorders (4th ed., DSM IV). Washington, DC: APA]. It consists of more frequent involvement, increased persistence and elevated monetary risk in an effort to recoup money that has been lost. The present research makes a distinction between within- and between-session chasing, and attempts to examine the determinants of within-session chasing in a reasonably valid laboratory analogue. Male college students who gambled (N=248) were given US10 (nonplayers); (2) gamble and win even more money by quitting at a more optimal time (nonchasers); or (3) gamble longer than they should, resulting in the loss of all available money (chasers). Zuckerman's impulsivity factor (from the ZKPQ) discriminated chasers from nonchasers. Breen and Zuckerman's Gambling Beliefs and Attitudes Survey (GABS) discriminated players from nonplayers. A sensation-seeking explanation of within-session chasing was not supported. The results are discussed in terms of the impact of impulsivity on within-session chasing. It is suggested that the GABS may serve as a general measure of `affinity' to gambling, and could be useful in examining the process of between-session chasing, that is, returning on another day to `get even'.
Agent-based modeling is a powerful simulation modeling technique that has seen a number of applications in the last few years, including applications to real-world business problems. After the basic principles of agent-based simulation are briefly introduced, its four areas of application are discussed by using real-world applications: flow simulation, organizational simulation, market simulation, and diffusion simulation. For each category, one or several business applications are described and analyzed.
"Chasing ones losses" is a key symptom among pathological gamblers (PGs). This study focuses on quantitative differences in episodic chasing (i.e., sequences of disadvantageous decisions within a single gambling session) between PGs and non-pathological gamblers (NPGs). We compared 61 PGs and 39 NPGs on the Iowa Gambling Task (IGT) and the Zuckerman Sensation Seeking Scale (SSS). The PGs showed significantly more chasing and had significantly poorer decision-making strategies than NPGs, particularly among males (F = 4.52, p < 0.05). Random players were significantly less sensation seeking than advantageous and disadvantageous (i.e., chasing) players, but there was no interaction with group or gender. The results suggest that quantifiable within-session gambling behavior holds important implications for detecting underlying vulnerabilities to gambling pathology.