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on Evolutionary Economics |
| By: | Davis, John B. (Department of Economics Marquette University); (Department of Economics Marquette University) |
| Abstract: | Compares the mainstream and Institutional and evolutionary economics views of individual and collective behavior. Describes the methodological assumptions for each. Distinguishes the two opposed approaches conceptions of time they employ. Explains the role social identity applies in explaining two opposed approaches adopt different conceptions of time. Closes with an explanatory dilemma the subject creates. |
| Keywords: | individual behavior, collective behavior, mainstream econonmics, Institutional and evolutionary economics |
| JEL: | B31 B41 B52 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:mrq:wpaper:2025-04 |
| By: | Egil Diau |
| Abstract: | The origins of economic behavior remain unresolved-not only in the social sciences but also in AI, where dominant theories often rely on predefined incentives or institutional assumptions. Contrary to the longstanding myth of barter as the foundation of exchange, converging evidence from early human societies suggests that reciprocity-not barter-was the foundational economic logic, enabling communities to sustain exchange and social cohesion long before formal markets emerged. Yet despite its centrality, reciprocity lacks a simulateable and cognitively grounded account. Here, we introduce a minimal behavioral framework based on three empirically supported cognitive primitives-individual recognition, reciprocal credence, and cost--return sensitivity-that enable agents to participate in and sustain reciprocal exchange, laying the foundation for scalable economic behavior. These mechanisms scaffold the emergence of cooperation, proto-economic exchange, and institutional structure from the bottom up. By bridging insights from primatology, developmental psychology, and economic anthropology, this framework offers a unified substrate for modeling trust, coordination, and economic behavior in both human and artificial systems. For an interactive visualization of the framework, see: https://egil158.github.io/cogfoundations -econ/ |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.02945 |
| By: | Federico Innocenti; Roberto Rozzi |
| Abstract: | We study the performance of different methods for processing information, incorporating narrative selection within an evolutionary model. All agents update their beliefs according to Bayes' Rule, but some strategically choose the narrative they use in updating according to heterogeneous criteria. We simulate the endogenous composition of the population, considering different laws of motion for the underlying state of the world. We find that conformists -- that is, agents that choose the narrative to conform to the average belief in the population -- have an evolutionary advantage over other agents across all specifications. The survival chances of the remaining types depend on the uncertainty regarding the state of the world. Agents who tend to develop mild beliefs perform better when the uncertainty is high, whereas agents who tend to develop extreme beliefs perform better when the uncertainty is low. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.03540 |
| By: | Pawe{\l} Niszczota; Tomasz Grzegorczyk; Alexander Pastukhov |
| Abstract: | Machines driven by large language models (LLMs) have the potential to augment humans across various tasks, a development with profound implications for business settings where effective communication, collaboration, and stakeholder trust are paramount. To explore how interacting with an LLM instead of a human might shift cooperative behavior in such settings, we used the Prisoner's Dilemma game -- a surrogate of several real-world managerial and economic scenarios. In Experiment 1 (N=100), participants engaged in a thirty-round repeated game against a human, a classic bot, and an LLM (GPT, in real-time). In Experiment 2 (N=192), participants played a one-shot game against a human or an LLM, with half of them allowed to communicate with their opponent, enabling LLMs to leverage a key advantage over older-generation machines. Cooperation rates with LLMs -- while lower by approximately 10-15 percentage points compared to interactions with human opponents -- were nonetheless high. This finding was particularly notable in Experiment 2, where the psychological cost of selfish behavior was reduced. Although allowing communication about cooperation did not close the human-machine behavioral gap, it increased the likelihood of cooperation with both humans and LLMs equally (by 88%), which is particularly surprising for LLMs given their non-human nature and the assumption that people might be less receptive to cooperating with machines compared to human counterparts. Additionally, cooperation with LLMs was higher following prior interaction with humans, suggesting a spillover effect in cooperative behavior. Our findings validate the (careful) use of LLMs by businesses in settings that have a cooperative component. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.18639 |
| By: | Martin Jaraiz |
| Abstract: | This paper presents a novel Darwinian Agent-Based Modeling (ABM) methodology formacroeconomic forecasting that leverages evolutionary principles to achieve remarkablecomputational efficiency and emergent realism. Unlike conventional DSGE and ABM approachesthat rely on complex behavioral rules derived from large firm analysis, our framework employssimple "common sense" rules representative of small firms directly serving final consumers. Themethodology treats households as the primary drivers of economic dynamics, with firms adaptingthrough market-based natural selection within limited interaction neighborhoods. We demonstrate that this approach, when constrained by Input-Output table structures, generates realistic economic patterns including wealth distributions, firm size distributions, andsectoral employment patterns without extensive parameter calibration. Using FIGARO Input-Output tables for 46 countries and focusing on Austria as a case study, we show that the modelreproduces empirical regularities while maintaining computational efficiency on standard laptopsrather than requiring supercomputing clusters. Key findings include: (1) emergence of realistic firm and employment distributions fromminimal behavioral assumptions, (2) accurate reproduction of the initial Social Accounting Matrixvalues through evolutionary dynamics, (3) successful calibration using only 5-6 country-specificparameters to complement the FIGARO data, and (4) computational performance enabling fullsimulations on consumer hardware. These results suggest that evolutionary ABM approaches canprovide robust policy insights by capturing decentralized market adaptations while avoiding thecomputational complexity of traditional DSGE and comprehensive ABM models. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.04074 |
| By: | Ece, Onur |
| Abstract: | The lack of a universal, physically grounded definition of life remains a critical gap across biology, astrobiology, and artificial intelligence. Traditional definitions often rely on biochemical functions or evolutionary heuristics, offering limited applicability across substrates, scales, and domains. This paper introduces a general law: a system is alive if and only if it sustains a positive rate of entropy resistance. In the quantum regime, this is formalized as Rq (t) =−d/dt Tr[ρ(t) ln ρ(t)] >0, where ρ(t) is the system’s density matrix and the trace yields the von Neu- mann entropy. The proposed condition is substrate-independent, operationally measurable, and falsifiable. This formulation provides a unifying thermodynamic criterion for terrestrial biology, synthetic organisms, coherent quantum states, and potential extraterres- trial systems—without invoking replication, metabolism, or evolution. It reframes life not as a biological artifact, but as a distinct physical regime that persistently resists informational and entropic collapse. Life, under this framework, is not explained by physics. It is a phase of physics, defined by its sustained resistance to the default trajectory of the universe. |
| Date: | 2025–07–29 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:r9826_v5 |
| By: | Avner Greif; Joel Mokyr; Guido Tabellini |
| Abstract: | Why did the industrial revolution occur in Europe and not in China, despite China being well ahead of Europe in terms of economic and technological achievements several centuries earlier? We revisit this long-standing question from a new perspective. We emphasize the importance of the different social organizations that diffused in these two parts of the world in the centuries that preceded the industrial revolution: kin-based organizations in China, vs corporations in Europe. We explain their cultural origins, and discuss how these different organizations shaped the evolution of legal systems, political institutions and human capital accumulation in these two parts of the world. Our main argument is that European corporations played a crucial role in the scientific and technological innovations that ultimately led to the industrial revolution. |
| Keywords: | industrial revolution, China, Europe, culture, institutions, organizations |
| JEL: | N00 P00 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12023 |
| By: | Egil Diau |
| Abstract: | A central challenge in economics and artificial intelligence is explaining how financial behaviors-such as credit, insurance, and trade-emerge without formal institutions. We argue that these functions are not products of institutional design, but structured extensions of a single behavioral substrate: reciprocity. Far from being a derived strategy, reciprocity served as the foundational logic of early human societies-governing the circulation of goods, regulation of obligation, and maintenance of long-term cooperation well before markets, money, or formal rules. Trade, commonly regarded as the origin of financial systems, is reframed here as the canonical form of reciprocity: simultaneous, symmetric, and partner-contingent. Building on this logic, we reconstruct four core financial functions-credit, insurance, token exchange, and investment-as expressions of the same underlying principle under varying conditions. By grounding financial behavior in minimal, simulateable dynamics of reciprocal interaction, this framework shifts the focus from institutional engineering to behavioral computation-offering a new foundation for modeling decentralized financial behavior in both human and artificial agents. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.00099 |
| By: | Diaz Sanchez, Jose Luis |
| Abstract: | Hedonic adaptation—the tendency to return to a baseline level of well-being after changes in life circumstances—offers a new perspective on theodicy, the attempt to reconcile suffering with a benevolent, omnipotent, and omniscient God. Since perceived suffering tends to revert to baseline, reductions in actual suffering may provide only temporary relief. This paper develops a simplified theoretical model, drawing on economic methods, to analyze how perceived suffering evolves over time, whether adjusting adaptation speeds could reduce distress, and what this reveals about the normative limits of benevolent intervention. The model demonstrates a structural trade-off: while slower adaptation may extend relief, it can also intensify distress during hardship. These dynamics lend support to soul-making theodicies by showing how persistent suffering fosters resilience and moral growth, and they echo free will theodicies by portraying adaptation as a built-in human feature, shaped by evolutionary pressures. At the same time, it challenges interventionist theodicies by emphasizing that suffering may persist despite benevolent efforts. It thereby invites greater attention to the recurrence of suffering, not only its intensity, as a concern for theodical reflection. |
| Keywords: | Hedonic adaptation; perceived suffering; philosophy of religion; economic modeling; normative constraints. |
| JEL: | A12 B4 D03 D91 Z10 Z12 |
| Date: | 2025–07–03 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125400 |
| By: | Nicolas L. Bottan; Ricardo Perez-Truglia; Hitoshi Shigeoka; Katsunori Yamada |
| Abstract: | Preferences for status are typically attributed to two distinct channels: self-image, in which individuals derive utility from being richer than others, and social-image, in which individuals value being seen as richer by others. While both channels are believed to be at play, little is known about their relative importance. We address this gap using a hypothetical discrete choice experiment. Our findings indicate that self-image is at most 19.3% as important as social-image. Additionally, we document substantial heterogeneity in the strength of these preferences across individuals and domains. |
| JEL: | C9 Z13 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34094 |
| By: | Margarita Leib; Nils K\"obis; Ivan Soraperra |
| Abstract: | People increasingly rely on AI-advice when making decisions. At times, such advice can promote selfish behavior. When individuals abide by selfishness-promoting AI advice, how are they perceived and punished? To study this question, we build on theories from social psychology and combine machine-behavior and behavioral economic approaches. In a pre-registered, financially-incentivized experiment, evaluators could punish real decision-makers who (i) received AI, human, or no advice. The advice (ii) encouraged selfish or prosocial behavior, and decision-makers (iii) behaved selfishly or, in a control condition, behaved prosocially. Evaluators further assigned responsibility to decision-makers and their advisors. Results revealed that (i) prosocial behavior was punished very little, whereas selfish behavior was punished much more. Focusing on selfish behavior, (ii) compared to receiving no advice, selfish behavior was penalized more harshly after prosocial advice and more leniently after selfish advice. Lastly, (iii) whereas selfish decision-makers were seen as more responsible when they followed AI compared to human advice, punishment between the two advice sources did not vary. Overall, behavior and advice content shape punishment, whereas the advice source does not. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.19487 |