From rational actor to efficient complexity manager: Exorcising the ghost of Homo economicus with a unified synthesis of cognition research

Jordan Levine, Kai M.A. Chan and Terre Satterfield

Ecological Economics, 2015, vol. 114, issue C, 22-32

Abstract: It is now commonplace to note that economics' canonical model of humans as rational, self-interested utility-maximizers (Homo economicus) is both descriptively misleading, and often insufficiently predictive. However, certain outdated assumptions tied to Homo economicus persist, often influencing discourse and research design even in sustainability-oriented fields. We argue this ‘ghost’ of Homo economicus endures because the diversity of findings that confound the canonical model has surfaced across multiple behavioral and cognitive sciences, each with its own terminology and focus area. As such, a unified, accessible synthesis of this new information has yet to emerge. In this paper we review recent insights from across the behavioral and cognitive sciences, and propose an ‘efficient complexity manager’ (ECM) model (Homo efficens) as the best synthesizing option. The crux of this model is that our species works within biological limits to efficiently filter massive environmental complexity. This is achieved largely through analogical—or ‘case-based’—reasoning. We explain this synthesized model using a series of accessible metaphors. Finally, we speculate on how this model may enrich future sustainable development research insofar as it points to fruitful units of analysis, can stimulate methodological innovation, and provide a more explicit theoretical foundation for the field.

Keywords: Unified model; Heuristics; Biases; Analogy; Mental models; Homo efficens (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolec:v:114:y:2015:i:c:p:22-32

DOI: 10.1016/j.ecolecon.2015.03.010

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