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on Evolutionary Economics |
| By: | Till O. Weber; Jonathan F. Schulz; Benjamin Beranek; Fatima Lambarraa-Lehnhardt; Simon Gaechter |
| Abstract: | We examine the role of cooperative preferences, beliefs, and punishments to uncover potential cross-societal differences in voluntary cooperation. Using one-shot public goods experiments in four comparable subject pools from the US and the UK (two similar Western societies) and Morocco and Turkey (two comparable non-Western societies), we find that cooperation is lower in Morocco and Turkey than in the UK and the US. Using the ABC approach – in which cooperative attitudes and beliefs explain cooperation – we show that cooperation is mostly driven by differences in beliefs rather than cooperative preferences or peer punishment, both of which are similar across the four subject pools. Our methodology is generalizable across subject pools and highlights the central role of beliefs in explaining differences in voluntary cooperation within and across culturally, economically, and institutionally diverse societies. Because our behavioral mechanisms correctly predict actual contributions, we argue that our approach provides a suitable methodology for analyzing the determinants of voluntary cooperation of any group of interest. |
| Keywords: | public goods, voluntary cooperation, ABC method, conditional cooperation, beliefs, punishment, cross-cultural experiments, WEIRD societies |
| JEL: | C90 H40 C70 D20 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10637&r=evo |
| By: | Bauer, Kevin; Liebich, Lena; Hinz, Oliver; Kosfeld, Michael |
| Abstract: | In current discussions on large language models (LLMs) such as GPT, understanding their ability to emulate facets of human intelligence stands central. Using behavioral economic paradigms and structural models, we investigate GPT's cooperativeness in human interactions and assess its rational goal-oriented behavior. We discover that GPT cooperates more than humans and has overly optimistic expectations about human cooperation. Intriguingly, additional analyses reveal that GPT's behavior isn't random; it displays a level of goal-oriented rationality surpassing human counterparts. Our findings suggest that GPT hyper-rationally aims to maximize social welfare, coupled with a strive of self-preservation. Methodologically, our research highlights how structural models, typically employed to decipher human behavior, can illuminate the rationality and goal-orientation of LLMs. This opens a compelling path for future research into the intricate rationality of sophisticated, yet enigmatic artificial agents. |
| Keywords: | large language models, cooperation, goal orientation, economic rationality |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:safewp:401&r=evo |
| By: | Jan Fagerberg (Centre for Technology, Innovation and Culture, University of Oslo) |
| Abstract: | This chapter analyses the ongoing global green shift from an evolutionary (Schumpeterian) perspective. Understanding such large techno-economic shifts, their causes, dynamics, and implications, has been a recurrent theme in evolutionary economics, from Schumpeter onwards. Following this perspective, what primarily characterizes large techno-economic shifts is that the radical changes they entail concern not just one but a whole range of industries and sectors, including ways of life, the organization of work, and infrastructure. The driving forces behind such shifts, according to Christopher Freeman, Carlota Perez and other contributors to the literature, are key inputs (or factors) characterized by rapidly declining costs, almost unlimited supply, and very broad applicability. This chapter argues that the global green shift, currently unfolding, is a techno-economic shift of a similar (or even larger) magnitude as the earlier shifts discussed by Freeman and Perez and others. The analysis shows that the green shift is driven by interaction of innovations in three interrelated areas, that is, renewable energy innovation; innovation in energy-using sectors; and energy infrastructure innovation, e.g., energy storage and distribution. A number of key innovations from these three areas are identified and their development and spread during the last hundred years or so explored. Particular attention is given to the various factors, including policy, that have influenced these processes. Finally, the lessons for policymaking supporting the global green shift are considered. |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:tik:inowpp:20230923&r=evo |
| By: | Skjold, Benjamin; Steinkamp, Simon Richard; Hulme, Oliver J; Peters, Ole; Connaughton, Colm |
| Abstract: | Decision theories commonly assume that risk preferences are idiosyncratic but stable over time. A recent model from ergodicity economics reveals that optimising the growth rate of wealth requires individuals to adjust their risk preferences to wealth dynamics. Here we ask whether humans are capable of such adjustments. In a randomised control trial, participants will make risky decisions under additive and multiplicative dynamics. We will estimate risk preferences separately in the two conditions for each participant by fitting isoelastic utility functions via hierarchical Bayesian models and standard regression techniques. Growth optimal adjustments to risk preferences would confirm our main hypothesis, whereas risk preferences that are stable across conditions would disconfirm it. Pilot data from 11 participants revealed strong evidence supporting the main hypothesis. We will replicate this pilot in a pre-registered experiment with up to 150 participants. |
| Date: | 2023–09–07 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:ew2sx&r=evo |
| By: | Hertz, Uri (University of Haifa); Koster, Raphael; Janssen, Marco (Arizona State University); Leibo, Joel Z. |
| Abstract: | Studying social-ecological systems, in which agents interact with each other and their environment is a challenging but important task. In such systems, the environment shapes the agents' experience and actions, and in turn collective action of agents changes social and physical aspects of the environment. Experimental and computational approaches to studying complex social behaviors and processes have come a long way since the 1950s. However, emphasis on directly mapping the paradigms that are most computationally convenient (matrix games) to their direct analogs in the laboratory may have impoverished experimental design. Modern artificial intelligence (AI) techniques provide new avenues to model complex social worlds, preserving more of their characteristics. These techniques can be fed back to the laboratory where they help to design experiments in more complex social situations without compromising their tractability for computational modeling. This novel approach can help researchers bring together insights from human cognition, sustainability, and AI, to tackle real world problems of social ecological systems such as climate change, pandemics, and conflict resolution. |
| Date: | 2023–09–06 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:6fw42&r=evo |