[Selections by Humberto Barreto for SHOE list.]
| on History and Philosophy of Economics |
| By: | Heise, Arne |
| Abstract: | The capitalism John Maynard Keynes struggled to analyse was clearly an industrial capitalism in which the investor used physical capital only to end up with more money than he started with. It is particularly the post Keynesian school of 'monetary or fundamentalist Keynesianism' which elaborated Keynes's monetary theory of production into an alternative economic paradigm that replaces the exchange ontology with an ontology based on nominal obligations. As economic history reports a higher speed of financial than real asset accumulation over the past half a century - a process often dubbed "financialisation" -, doubts have been raised as to whether this transformation of industrial capitalism into financialised capitalism demands for a new macroeconomic approach. |
| Keywords: | John Maynard Keynes,Hyman P. Minsky,monetary production economy,industrialcapitalism,financial capitalism,Financial Instability Hypothesis |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cessdp:96&r=hpe |
| By: | Naudé, Wim (RWTH Aachen University) |
| Abstract: | This paper contributes to the economics of AI by exploring three topics neglected by economists: (i) the notion of a Singularity (and Singleton), (ii) the existential risks that AI may pose to humanity, including that from an extraterrestrial AI in a Dark Forest universe; and (iii) the relevance of economics' Mythical Agent (homo economicus) for the design of value-aligned AI-systems. From the perspective of expected utility maximization, which both the fields of AI and economics share, these three topics are interrelated. By exploring these topics, several future avenues for economic research on AI becomes apparent, and areas where economic theory may benefit from a greater understanding of AI can be identified. Two further conclusions that emerge are first that a Singularity and existential risk from AI are still science fiction: which, however, should not preclude economics from bearing on the issues (it does not deter philosophers); and two, that economists should weigh in more on existential risk, and not leave this topic to lose credibility because of the Pascalian fanaticism of longtermism. |
| Keywords: | technology, artificial intelligence, economics, growth, existential risk, longtermism, Fermi Paradox, Grabby Aliens |
| JEL: | O40 O33 D01 D64 |
| Date: | 2022–11 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp15713&r=hpe |
| By: | Andrea Amelio; Florian Zimmermann |
| Abstract: | In this article, we review the economics literature on the motivated recall of information. Summarizing both theoretical and empirical work, we highlight key results this literature has produced. We also discuss methodological issues when studying motivated memory. We conclude by highlighting open questions and possibly exciting avenues for future research. |
| Keywords: | Memory, Beliefs, Motivated Reasoning, Review |
| JEL: | D01 |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:ajk:ajkdps:213&r=hpe |
| By: | Joachim Wagner |
| Abstract: | Starting in issue 1 of volume 236 (2016), the Jahrbücher für Nationalökonomie und Statistik / Journal of Economics and Statistics publish a special section entitled Data Observer. Contributions to this series describe data that can be used in empirical research in economics, and in the social sciences in general. While most of these data sets are micro data at the level of individuals, households, or firms (including linked employer-employee data sets), cross section and time series data at an aggregate level are covered as well. The purpose of the contributions to this section is to describe the information that is available in the data sets, to give examples of topics investigated with the data, and to inform readers how to access these data for their own research. The contributions are written by experts who often were in charge of collecting or building the data sets. Furthermore, papers in the series portray the research data centers and data service centers of data producing institutions that allow academic researchers to work with (mostly confidential) micro data for individuals and firms. |
| Keywords: | Data Observer |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:rsw:rswwps:rswwps279&r=hpe |
| By: | Huber, Christoph (WU Vienna University of Economics and Business); Litsios, Christos; Nieper, Annika S. (Vrije Universiteit Amsterdam); Promann, Timo |
| Abstract: | Transparency and observability have been shown to foster ethical decision-making as people tend to comply with an underlying norm for honesty. In a die-rolling experiment, we investigate whether observability can have detrimental effects, however, in situations implying a social norm for dishonesty. We thus introduce a norm nudge towards honesty or dishonesty and make participants' decisions observable and open to other participants' judgment in order to manipulate the observability of people's decisions as well as the underlying social norm. We find that a nudge towards honesty indeed increases the level of honesty, suggesting that such a norm nudge can successfully induce behavioral change. Our introduction of social image concerns via observability, however, does not affect honesty and does not interact with our norm nudge. |
| Date: | 2022–06–17 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:2nxv8&r=hpe |
| By: | Bonnefon, Jean-François; Purcell, Zoe |
| Abstract: | Artificial Intelligence (AI) can be harnessed to create sophisticated social and moral scoring systems —enabling people and organizations to form judgements of others at scale. However, it also poses significant ethical challenges and is, subsequently, the subject of wide debate. As these technologies are developed and governing bodies face regulatory decisions, it is crucial that we understand the attraction or resistance that people have for AI moral scoring. Across four experiments, we show that the acceptability of moral scoring by AI is related to expectations about the quality of those scores, but that expectations about quality are compromised by people's tendency to see themselves as morally peculiar. We demonstrate that people overestimate the peculiarity of their moral profile, believe that AI will neglect this peculiarity, and resist for this reason the introduction of moral scoring by AI. |
| JEL: | D91 |
| Date: | 2022–11–25 |
| URL: | http://d.repec.org/n?u=RePEc:tse:wpaper:127527&r=hpe |
| By: | Christoph Kuzmics (University of Graz, Austria); Brian W. Rogers (Washington University in St. Louis, U.S.A.); Xiannong Zhang (Washington University in St. Louis, U.S.A.) |
| Abstract: | The 1961 Ellsberg paradox is typically seen as an empirical challenge to the subjective expected utility framework. Experiments based on Ellsberg's design have spawned a variety of new approaches, culminating in a new paradigm represented by, now classical, models of ambiguity aversion. We design and implement a decision-theoretic lab experiment that is extremely close to the original Ellsberg design and in which, empirically, subjects make choices very similar to those in the Ellsberg experiments. In our environment, however, these choices cannot be rationalized by any of the classical models of ambiguity aversion. |
| Keywords: | Knightian uncertainty; subjective expected utility; ambiguity aversion; lab experiment. |
| JEL: | C91 D81 |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:grz:wpaper:2022-05&r=hpe |
| the School of Economics and Finance of Massey University in New Zealand. |