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on Utility Models and Prospect Theory |
| By: | Giebel, Frank |
| Abstract: | Der Artikel untersucht im Rahmen der bedarfswirtschaftlichen Betriebswirtschaftslehre (öffentliche Unternehmen, Genossenschaften, Stiftungsunternehmen) inwieweit Eigenkapitalzinsen bei der Berechnung der Selbstkostenpreise zu berücksichtigen sind und welche Rolle dabei die Idee der Opportunitätskosten spielt im Rahmen einer bedarfswirtschaftlichen Investitionstheorie. Es zeigt sich, dass für das Ziel der Nutzer-Nutzenmaximierung im Grundsatz keine Eigenkapitalzinsen in der Kalkulation von Selbstkostenpreisen anzusetzen sind. Es zeigt sich weiter, dass für das Ziel der Gesamtersparnismaximierung auf Leistungsabnehmerseite die Idee von Opportunitätskosten keine Rolle spielen. Sie könnten jedoch bei Fragen der Verteilung der Ersparnisse auf der Leistungsabnehmerseite bei bestimmten Konstellationen eine Rolle spielen. The article examines, within the framework of needs-based business economics (public enterprises, cooperatives, foundation-based businesses) , the extent to which interest on equity should be considered when calculating cost prices and the role that the concept of opportunity costs plays within a needs-based investment theory. It becomes evident that, for the purpose of utility maximization, imputed interest rates for the equity capital used should not be categorically included in the calculation of cost prices. Furthermore, the analysis reveals that, for the goal of maximizing overall savings on the side of the companies customers, the concept of opportunity costs does not play a role. However, in questions concerning the allocation of savings between customers, opportunity costs could play a certain role in specific circumstances. |
| Keywords: | Bedarfswirtschaft, bedarfswirtschaftliche Investitionstheorie, öffentliche Unternehmen, Genossenschaften, kalkulatorische Eigenkapitalverzinsung, Opportunitätskosten |
| JEL: | M1 M10 M2 M21 |
| Date: | 2025–03–24 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:124086 |
| By: | Eléonore Dodivers (Université Côte d'Azur, CNRS, GREDEG, France); Ismaël Rafaï (Toulouse School of Economics, Toulouse School of Management) |
| Abstract: | This paper investigates Artificial intelligence Large Language Models (AI-LLM) social preferences’ in Dictator Games. Brookins and Debacker (2024, Economics Bulletin) previously observed a tendency of ChatGPT-3.5 to give away half its endowment in a standard Dictator Game and interpreted this as an expression of fairness. We replicate their experiment and introduce a multiplicative factor on donations which varies the efficiency of the transfer. Varying transfer efficiency disentangles three donation explanations (inequality aversion, altruism, or focal point). Our results show that ChatGPT-3.5 donations should be interpreted as a focal point rather than the expression of fairness. In contrast, a more advanced version (ChatGPT-4o) made decisions that are better explained by altruistic motives than inequality aversion. Our study highlights the necessity to explore the parameter space, when designing experiments to study AI-LLM preferences. |
| Keywords: | Artificial Intelligence, Large Language Models, Dictator Games, Experimental Economics, Social Preferences |
| JEL: | D90 O33 C02 C91 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:gre:wpaper:2025-09 |
| By: | Miyake, Yusuke |
| Abstract: | This study investigates how Artificial Intelligence (AI) affects fertility decisions, economic growth, and overall social welfare. Despite substantial technological progress and increases in economic output (GDP), advanced economies, notably Japan, face severe demographic challenges due to dramatically declining fertility rates. This phenomenon raises important questions regarding the traditional measures of economic prosperity, prompting a re-evaluation of GDP as a reliable indicator of social welfare. To address these issues, this article develops a dynamic economic growth model incorporating heterogeneous human capital (skilled and unskilled labor) and introduces AI as a new, distinct form of capital investment. Unlike traditional physical capital, AI capital features negligible depreciation rates, significantly altering investment decisions, and long-term growth dynamics. On the demand side, households optimize their utility by allocating their limited time between labor supply, leisure, and child-rearing activities, directly influencing fertility rates and human capital accumulation. This paper argues that AI-driven algorithms fundamentally improve market efficiency by precisely matching heterogeneous consumer preferences and supplier characteristics, leading to optimal resource allocation. Unlike the traditional ”law of one price, ” algorithm-driven markets generate multiple equilibrium prices, varying according to individual preferences and attributes, characterized herein as a shift toward a ”law of multiple prices.” The analysis suggests critical policy implications, emphasizing the need for refined economic and educational policies that address the implications of AI-driven market dynamics on fertility choices and income distribution. In particular, policy interventions must strategically promote educational reforms that diversify and enrich human capital, aligning it more closely with the demands of AI-intensive industries. This model provides a theoretical framework for understanding the intricate interplay between AI, demographic shifts, economic inequality, and long-term growth trajectories. |
| Keywords: | Artificial Intelligence, Fertility Decline, Endogenous Growth, Algorithmic Economics, Human Capital, Social Welfare |
| JEL: | J13 J24 O33 O4 O41 |
| Date: | 2025–04–04 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:124245 |