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on Evolutionary Economics |
| By: | Nick Obradovich; Ömer Özak; Ignacio Martín; Ignacio Ortuño-Ortín; Edmond Awad; Manuel Cebrián; Rubén Cuevas; Klaus Desmet; Iyad Rahwan; Ángel Cuevas |
| Abstract: | Culture has played a pivotal role in human evolution. Yet, the ability of social scientists to study culture is limited by the currently available measurement instruments. Scholars of culture must regularly choose between scalable but sparse survey-based methods or restricted but rich ethnographic methods. Here, we demonstrate that massive online social networks can advance the study of human culture by providing quantitative, scalable, and high-resolution measurement of behaviorally revealed cultural values and preferences. We employ publicly available data across nearly 60,000 topic dimensions drawn from two billion Facebook users across 225 countries and territories. We first validate that cultural distances calculated from this measurement instrument correspond to traditional survey-based and objective measures of cross-national cultural differences. We then demonstrate that this expanded measure enables rich insight into the cultural landscape globally at previously impossible resolution. We analyze the importance of national borders in shaping culture, explore unique cultural markers that identify subnational population groups, and compare subnational divisiveness to gender divisiveness across countries. The global collection of massive data on human behavior provides a high-dimensional complement to traditional cultural metrics. Further, the granularity of the measure presents enormous promise to advance scholars' understanding of additional fundamental questions in the social sciences. The measure enables detailed investigation into the geopolitical stability of countries, social cleavages within both small and large-scale human groups, the integration of migrant populations, and the disaffection of certain population groups from the political process, among myriad other potential future applications. |
| JEL: | C80 J10 J16 O10 R10 Z10 |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:27827&r=all |
| By: | Mads Kock Pedersen; Carlos Mauricio Casta\~no D\'iaz; Mario Alejandro Alba-Marrugo; Ali Amidi; Rajiv Vaid Basaiawmoit; Carsten Bergenholtz; Morten H. Christiansen; Miroslav Gajdacz; Ralph Hertwig; Byurakn Ishkhanyan; Kim Klyver; Nicolai Ladegaard; Kim Mathiasen; Christine Parsons; Michael Bang Petersen; Janet Rafner; Anders Ryom Villadsen; Mikkel Wallentin; Jacob Friis Sherson; Skill Lab players |
| Abstract: | Psychology and the social sciences are undergoing a revolution: It has become increasingly clear that traditional lab-based experiments fail to capture the full range of differences in cognitive abilities and behaviours across the general population. Some progress has been made toward devising measures that can be applied at scale across individuals and populations. What has been missing is a broad battery of validated tasks that can be easily deployed, used across different age ranges and social backgrounds, and employed in practical, clinical, and research contexts. Here, we present Skill Lab, a game-based approach allowing the efficient assessment of a suite of cognitive abilities. Skill Lab has been validated outside the lab in a crowdsourced population-size sample recruited in collaboration with the Danish Broadcast Company (Danmarks Radio, DR). Our game-based measures are five times faster to complete than the equivalent traditional measures and replicate previous findings on the decline of cognitive abilities with age in a large population sample. Furthermore, by combining the game data with an in-game survey, we demonstrate that this unique dataset has implication for key questions in social science, challenging the Jack-of-all-Trades theory of entrepreneurship and provide evidence for risk preference being independent of executive functioning. |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2009.05274&r=all |
| By: | Matthijs van Veelen (University of Amsterdam) |
| Abstract: | Most of the literature on the evolution of human pro-sociality looks at reasons why evolution made us not play the Nash equilibrium in prisoners’ dilemmas or public goods games. We suggest that in order to understand human morality, and human prosocial behaviour, we should look at reasons why evolution made us not play the subgame perfect Nash equilibrium in sequential games, such as the ultimatum game and the trust game. The “rationally irrational†behavior that can evolve in those games is a better match with actual human behaviour, including ingredients of morality such as honesty, responsibility, and sincerity, and also less nice properties, such as anger, as well as the incidence of conflict. Moreover, it can not only explain why humans have evolved to know wrong from right, but also why other animals, with similar population structures and similar rates of repetition, have not evolved the morality that humans have. |
| JEL: | C73 |
| Date: | 2020–09–22 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20200063&r=all |
| By: | Ryan Chahrour (Boston College); Kyle Jurado (Duke University) |
| Abstract: | Agents have foresight when they receive information about a random process above and beyond the information contained in its current and past history. In this paper, we propose an information-theoretic measure of the quantity of foresight in an information structure, and show how to separate informational assumptions about foresight from physical assumptions about the dynamics of the processes itself. We then develop a theory of endogenous foresight in which the type of foresight is chosen optimally by economic agents. In a prototypical dynamic model of consumption and saving, we derive a closed-form solution to the optimal foresight problem. |
| Keywords: | expectations; rational inattention; incomplete information; noise shocks |
| JEL: | D83 D84 E21 |
| Date: | 2020–09–10 |
| URL: | http://d.repec.org/n?u=RePEc:boc:bocoec:1017&r=all |
| By: | Cosmin L. Ilut; Rosen Valchev |
| Abstract: | We develop a tractable model of limited cognitive perception of the optimal policy function, with agents using costly reasoning effort to update beliefs about this optimal mapping of economic states into actions. A key result is that agents reason less (more) when observing usual (unusual) states, producing state- and history-dependent behavior. Our application is a standard incomplete markets model with ex-ante identical agents that hold no a-priori behavioral biases. The resulting ergodic distribution of actions and beliefs is characterized by “learning traps”, where locally stable dynamics of wealth generate “familiar” regions of the state space within which behavior appears to follow past-experience-based heuristics. We show qualitatively and quantitatively how these traps have empirically desirable properties: the marginal propensity to consume is higher, hand-to-mouth status is more frequent and persistent, and there is more wealth inequality than in the standard model. |
| JEL: | C11 D83 D91 E21 |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:27820&r=all |
| By: | Maarten Buis (University of Konstanz) |
| Abstract: | An Agent Based Model (ABM) is a simulation in which agents that each follow simple rules interact with one another and thus produce an often surprising outcome at the macro level. The purpose of an ABM is to explore mechanisms through which actions of the individual agents add up to a macro outcome by varying the rules that agents have to follow or varying with whom the agent can interact (for example, varying the network). These models have many applications, like the study of segregation of neighborhoods or the adoption of new technologies. However, the application that is currently most topical is the spread of a disease. In this talk, I will give introduction on how to implement an ABM in Mata, by going through the simple models I (a sociologist, not an epidemiologist) used to make sense of what is happening with the COVID-19 pandemic.Creation-Date: 20200911 |
| Date: | 2020–09–11 |
| URL: | http://d.repec.org/n?u=RePEc:boc:usug20:03&r=all |