Cultural Neuroeconomics of Intertemporal Choice

According to theories of cultural neuroscience, Westerners and Easterners may have distinct styles of cognition (e.g., different allocation of attention). Previous research has shown that Westerners and Easterners tend to utilize analytical and holistic cognitive styles, respectively. On the other hand, little is known regarding the cultural differences in neuroeconomic behavior. For instance, economic decisions may be affected by cultural differences in neurocomputational processing underlying attention; however, this area of neuroeconomics has been largely understudied. In the present paper, we attempt to bridge this gap by considering the links between the theory of cultural neuroscience and neuroeconomic theory\ud of the role of attention in intertemporal choice. We predict that (i) Westerners are more impulsive and inconsistent in intertemporal choice in comparison to Easterners, and (ii) Westerners more steeply discount delayed monetary losses than Easterners. We examine these predictions by utilizing a novel temporal discounting model based on Tsallis' statistics (i.e. a q-exponential model). Our preliminary analysis of temporal discounting of gains and losses by Americans and Japanese confirmed the predictions from the cultural neuroeconomic theory. Future study directions, employing computational modeling via neural networks, are briefly outlined and discussed

The challenge of complexity for cognitive systems

Complex cognition addresses research on (a) high-level cognitive processes – mainly problem solving, reasoning, and decision making – and their interaction with more basic processes such as perception, learning, motivation and emotion and (b) cognitive processes which take place in a complex, typically dynamic, environment. Our focus is on AI systems and cognitive models dealing with complexity and on psychological findings which can inspire or challenge cognitive systems research. In this overview we first motivate why we have to go beyond models for rather simple cognitive processes and reductionist experiments. Afterwards, we give a characterization of complexity from our perspective. We introduce the triad of cognitive science methods – analytical, empirical, and engineering methods – which in our opinion have all to be utilized to tackle complex cognition. Afterwards we highlight three aspects of complex cognition – complex problem solving, dynamic decision making, and learning of concepts, skills and strategies. We conclude with some reflections about and challenges for future research

Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making

It is widely believed that one's peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the pattern by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game makes a decision among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information

Objective measures of complexity

Mesures Objectives de la Complexité pour la Prise de Décision Dynamique. La gestion efficace de systèmes sociotechniques complexes dépend d’une compréhension des interrelations dynamiques entre les composantes de ces systèmes, de leur évolution à travers le temps, ainsi que du degré d’incertitude auquel les décideurs sont exposés. Quelles sont les caractéristiques de la prise de décision complexe qui ont un impact sur la performance humaine dans l’environnement moderne du travail, constamment en fluctuation et sous la pression du temps, exerçant de lourdes demandes sur la cognition ? La prise de décision complexe est un concept issu de la macrocognition, impliquant des processus et des fonctions de bas et haut niveaux de description tels que la métacognition, soit pour un individu de penser à propos de son propre processus de pensées. Dans le cas particulier de la prise de décision complexe, ce phénomène est nommé la pensée systémique. L’étude de la prise de décision complexe en dehors de l’environnement traditionnel du laboratoire, permettant un haut niveau de contrôle mais un faible degré de réalisme, est malheureusement difficile et presque impossible. Une méthode de recherche plus appropriée pour la macrocognition est l’expérimentation basée sur la simulation, à l’aide de micromondes numérisés sous la forme de jeux sérieux. Ce paradigme de recherche est nommé la prise de décision dynamique (PDD), en ce qu’il tient compte des caractéristiques de problèmes de prise de décision complexe telles que des séquences complexes de décisions et de changements d’états d’un problème interdépendants, qui peuvent changer de façon spontanée ou comme conséquence de décisions préalables, et pour lesquels la connaissance et la compréhension du décideur peut n’être que partielle ou incertaine. Malgré la quantité de recherche concernant la PDD à propos des difficultés encourues pour la performance humaine face à des problèmes de prise de décision complexe, l’acquisition de connaissances à propos de systèmes complexes, et à savoir si le transfert de l’apprentissage est possible, il n’existe pas de mesure quantitative de ce en quoi un problème de décision est considéré comme étant complexe. La littérature scientifique mentionne des éléments qualitatifs concernant les systèmes complexes (tels que des interrelations dynamiques, une évolution non-linéaire d’un système à travers le temps, et l’incertitude à propos des états d’un système et des issues des décisions), mais des mesures quantitatives et objectives exprimant la complexité de problèmes de décision n’ont pas été développées. Cette dissertation doctorale présente les concepts, la méthodologie et les résultats impliqués dans un projet de recherche visant à développer des mesures objectives de la complexité basées sur les caractéristiques de problèmes de prise de décision dynamique pouvant expliquer et prédire la performance humaine. En s’inspirant de divers domaines d’application de la théorie de la complexité tels que la complexité computationnelle, la complexité systémique, et l’informatique cognitive, un modèle formel des paramètre de la complexité pour des tâches de prise de décision dynamique a été élaboré. Un ensemble de dix mesures objectives de la complexité a été développé, consistant en des mesures de la complexité structurelle, des mesures de la complexité informationnelle, la complexité de la charge cognitive, et des mesures de la difficulté d’un problème, de la non-linéarité des relations, de l’incertitude concernant l’information et les décisions, ainsi qu’une mesure de l’instabilité d’un système dynamique sous des conditions d’inertie. Une analyse des résultats expérimentaux colligés à partir de cinq scénarios de PDD révèle qu’un nombre restreint de candidats parmi des modèles de régression linéaires multiple permet d’expliquer et de prédire les résultats de performance humaine, mais au prix de certaines violations des postulats de l’approche classique de la régression linéaire. De plus, ces mesures objectives de la complexité présentent un degré élevé de multicolinéarité, causée d’une part par l’inclusion de caractéristiques redondantes dans les calculs, et d’autre part par une colinéarité accidentelle imputable à la conception des scénarios de PDD. En tenant compte de ces deux considérations ainsi que de la variance élevée observée dans les processus macrocognitifs impliqués dans la prise de décision complexe, ces modèles présentent des valeurs élevées pour le terme d’erreur exprimant l’écart entre les observations et les prédictions des modèles. Une analyse additionnelle explore l’utilisation de méthodes alternatives de modélisation par régression afin de mieux comprendre la relation entre les paramètres de la complexité et les données portant sur performance humaine. Nous avons d’abord opté pour une approche de régression robuste afin d’augmenter l’efficience de l’analyse de régression en utilisant une méthode réduisant la sensibilité des modèles de régression aux observations influentes. Une seconde analyse élimine la source de variance imputable aux différences individuelles en focalisant exclusivement sur les effets imputables aux conditions expérimentales. Une dernière analyse utilise des modèles non-linéaires et non-paramétriques afin de pallier les postulats de la modélisation par régression, à l’aide de méthodes d’apprentissage automatique (machine learning). Les résultats suggèrent que l’approche de régression robuste produit des termes d’erreur substantiellement plus faibles, en combinaison avec des valeurs élevées pour les mesures de variance expliquée dans les données de la performance humaine. Bien que les méthodes non-linéaires et non-paramétriques produisent des modèles marginalement plus efficients en comparaison aux modèles de régression linéaire, la combinaison de ces modèles issus du domaine de l’apprentissage automatique avec les données restreintes aux effets imputables aux conditions expérimentales produit les meilleurs résultats relativement à l’ensemble de l’effort de modélisation et d’analyse de régression. Une dernière section présente un programme de recherche conçu pour explorer l’espace des paramètres pour les mesures objectives de la complexité avec plus d’ampleur et de profondeur, afin d’appréhender les combinaisons des caractéristiques des problèmes de prise de décision complexe qui sont des facteurs déterminants de la performance humaine. Les discussions concernant l’approche expérimentale pour la PDD, les résultats de l’expérimentation relativement aux modèles de régression, ainsi qu’à propos de l’investigation de méthodes alternatives visant à réduire la composante de variance menant à la disparité entre les observations et les prédictions des modèles suggèrent toutes que le développement de mesures objectives de la complexité pour la performance humaine dans des scénarios de prise de décision dynamique est une approche viable à l’approfondissement de nos connaissances concernant la compréhension et le contrôle exercés par un être humain face à des problèmes de décision complexe.Objective Measures of Complexity for Dynamic Decision-Making. Managing complex sociotechnical systems depends on an understanding of the dynamic interrelations of such systems’ components, their evolution over time, and the degree of uncertainty to which decision makers are exposed. What features of complex decision-making impact human performance in the cognitively demanding, ever-changing and time pressured modern workplaces? Complex decision-making is a macrocognitive construct, involving low to high cognitive processes and functions, such as metacognition, or thinking about one’s own thought processes. In the particular case of complex decision-making, this is called systems thinking. The study of complex decision-making outside of the controlled, albeit lacking in realism, traditional laboratory environment is difficult if not impossible. Macrocognition is best studied through simulation-based experimentation, using computerized microworlds in the form of serious games. That research paradigm is called dynamic decision-making (DDM), as it takes into account the features of complex decision problems, such as complex sequences of interdependent decisions and changes in problem states, which may change spontaneously or as a consequence of earlier decisions, and for which the knowledge and understanding may be only partial or uncertain. For all the research in DDM concerning the pitfalls of human performance in complex decision problems, the acquisition of knowledge about complex systems, and whether a learning transfer is possible, there is no quantitative measure of what constitutes a complex decision problem. The research literature mentions the qualities of complex systems (a system’s dynamical relationships, the nonlinear evolution of the system over time, and the uncertainty about the system states and decision outcomes), but objective quantitative measures to express the complexity of decision problems have not been developed. This dissertation presents the concepts, methodology, and results involved in a research endeavor to develop objective measures of complexity based on characteristics of dynamic decision-making problems which can explain and predict human performance. Drawing on the diverse fields of application of complexity theory such as computational complexity, systemic complexity, and cognitive informatics, a formal model of the parameters of complexity for dynamic decision-making tasks has been elaborated. A set of ten objective measures of complexity were developed, ranging from structural complexity measures, measures of information complexity, the cognitive weight complexity, and measures of problem difficulty, nonlinearity among relationships, information and decision uncertainty, as well as a measure of the dynamical system’s instability under inertial conditions. An analysis of the experimental results gathered using five DDM scenarios revealed that a small set of candidate models of multiple linear regression could explain and predict human performance scores, but at the cost of some violations of the assumptions of classical linear regression. Additionally, the objective measures of complexity exhibited a high level of multicollinearity, some of which were caused by redundant feature computation while others were accidentally collinear due to the design of the DDM scenarios. Based on the aforementioned constraints, and due to the high variance observed in the macrocognitive processes of complex decision-making, the models exhibited high values of error in the discrepancy between the observations and the model predictions. Another exploratory analysis focused on the use of alternative means of regression modeling to better understand the relationship between the parameters of complexity and the human performance data. We first opted for a robust regression analysis to increase the efficiency of the regression models, using a method to reduce the sensitivity of candidate regression models to influential observations. A second analysis eliminated the within-treatment source of variance in order to focus exclusively on between-treatment effects. A final analysis used nonlinear and non-parametric models to relax the regression modeling assumptions, using machine learning methods. It was found that the robust regression approach produced substantially lower error values, combined with high measures of the variance explained for the human performance data. While the machine learning methods produced marginally more efficient models of regression for the same candidate models of objective measures of complexity, the combination of the nonlinear and non-parametric methods with the restricted between-treatment dataset yielded the best results of all of the modeling and analyses endeavors. A final section presents a research program designed to explore the parameter space of objective measures of complexity in more breadth and depth, so as to weight which combinations of the characteristics of complex decision problems are determinant factors on human performance. The discussions about the experimental approach to DDM, the experimental results relative to the regression models, and the investigation of further means to reduce the variance component underlying the discrepancy between the observations and the model predictions all suggest that establishing objective measures of complexity for human performance in dynamic decision-making scenarios is a viable approach to furthering our understanding of a decision maker’s comprehension and control of complex decision problems

Risk preferences and development revisited

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Games for a new climate: experiencing the complexity of future risks

This repository item contains a single issue of the Pardee Center Task Force Reports, a publication series that began publishing in 2009 by the Boston University Frederick S. Pardee Center for the Study of the Longer-Range Future.This report is a product of the Pardee Center Task Force on Games for a New Climate, which met at Pardee House at Boston University in March 2012. The 12-member Task Force was convened on behalf of the Pardee Center by Visiting Research Fellow Pablo Suarez in collaboration with the Red Cross/Red Crescent Climate Centre to “explore the potential of participatory, game-based processes for accelerating learning, fostering dialogue, and promoting action through real-world decisions affecting the longer-range future, with an emphasis on humanitarian and development work, particularly involving climate risk management.” Compiled and edited by Janot Mendler de Suarez, Pablo Suarez and Carina Bachofen, the report includes contributions from all of the Task Force members and provides a detailed exploration of the current and potential ways in which games can be used to help a variety of stakeholders – including subsistence farmers, humanitarian workers, scientists, policymakers, and donors – to both understand and experience the difficulty and risks involved related to decision-making in a complex and uncertain future. The dozen Task Force experts who contributed to the report represent academic institutions, humanitarian organization, other non-governmental organizations, and game design firms with backgrounds ranging from climate modeling and anthropology to community-level disaster management and national and global policymaking as well as game design.Red Cross/Red Crescent Climate Centr

Predicted Causality in Decision Making: The Role of Culture

In the wider sense, decision making is embedded in the problem-solving process and its many stages (Davidson and Sternberg, 2003; Güss et al., 2010). In the narrow sense, decision making is understood as the ability to select one of several alternatives and to act accordingly (Güss 2004). Previous research has often focused on decision making in relatively predictable environments with clear goals (e.g., expected utility theory of von Neumann and Morgenstern, 1944). In recent decades the focus has been on decision making heuristics, i.e., strategies or rules of thumb, applied in uncertain situations (e.g., Tversky and Kahneman, 1974; Simon, 1979; Gigerenzer and Gaissmaier, 2011). Causality plays an important role in many cognitive processes – and causal cognition is itself influenced by culture (e.g., Norenzayan and Nisbett, 2000; Medin and Atran, 2004; Beller et al., 2009; Bender and Beller, 2011; for a controversial discussion of causal cognition, see Sperber et al., 1995). Causality is especially important during the decision-making process, because the decision maker has to predict what consequences specific decisions bring about before making a decision

Cognitive finance: Behavioural strategies of spending, saving, and investing.

Research in economics is increasingly open to empirical results. The advances in behavioural approaches are expanded here by applying cognitive methods to financial questions. The field of "cognitive finance" is approached by the exploration of decision strategies in the financial settings of spending, saving, and investing. Individual strategies in these different domains are searched for and elaborated to derive explanations for observed irregularities in financial decision making. Strong context-dependency and adaptive learning form the basis for this cognition-based approach to finance. Experiments, ratings, and real world data analysis are carried out in specific financial settings, combining different research methods to improve the understanding of natural financial behaviour. People use various strategies in the domains of spending, saving, and investing. Specific spending profiles can be elaborated for a better understanding of individual spending differences. It was found that people differ along four dimensions of spending, which can be labelled: General Leisure, Regular Maintenance, Risk Orientation, and Future Orientation. Saving behaviour is strongly dependent on how people mentally structure their finance and on their self-control attitude towards decision space restrictions, environmental cues, and contingency structures. Investment strategies depend on how companies, in which investments are placed, are evaluated on factors such as Honesty, Prestige, Innovation, and Power. Further on, different information integration strategies can be learned in decision situations with direct feedback. The mapping of cognitive processes in financial decision making is discussed and adaptive learning mechanisms are proposed for the observed behavioural differences. The construal of a "financial personality" is proposed in accordance with other dimensions of personality measures, to better acknowledge and predict variations in financial behaviour. This perspective enriches economic theories and provides a useful ground for improving individual financial services