Cost–benefit analysis of social/cultural learning in a nonstationary uncertain environment: An evolutionary simulation and an experiment with human subjects

• Published in: Evolution and human behavior Vol. 23; no. 5; pp. 373 - 393
• Main Authors: Kameda, Tatsuya, Nakanishi, Daisuke
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2002
• Subjects: Conformity; Cultural tradition; Cultural Transmission; Environment; Equilibrium; Ethology; Evolution; Game theory; Learning; Nonstationary environment; Producer–scrounger equilibrium; Simulation; Social Learning; Sociobiology; Social learning; Producer–scrounger equilibrium; Cultural transmission; Conformity; Nonstationary environment
• ISSN: 1090-5138; 1879-0607
• DOI: 10.1016/S1090-5138(02)00101-0

Social/cultural learning is an effective way to reduce uncertainty about the environment, helping individuals adopt an adaptive behavior cheaply. Although this is evident for learning about temporally stable targets, such as acquisition of a skill in avoiding toxic foods, the utility of social/cultural learning in a temporally unstable environment is less clear, since knowledge acquired by social learning may be outdated. This paper addresses the adaptive value of social/cultural learning in a nonstationary environment both theoretically and empirically. We first conducted an evolutionary computer simulation that extended Henrich and Boyd's [Evol. Hum. Behav. 19 (1998) 215.] model of cultural transmission, with the following results. When individual learning about the nonstationary environment is costly, a mixed equilibrium emerges in the population, where members who engage in costly individual learning and members who skip the information search and free-ride on other members' search efforts coexist at a stable ratio. Such a “producer–scrounger” structure qualifies effectiveness of social/cultural learning severely, especially “conformity bias” when using social information. We then tested these propositions by an experiment implementing a nonstationary uncertain environment in a laboratory. The results supported our thesis. Implications of these findings and some future directions are discussed.