An important challenge in collective systems is to understand how they are able to adapt in a decentralized manner to novel environmental conditions. Individuals in these systems must be able to acquire new behaviors autonomously (through natural evolution or learning), and they must be able to do so in an open-ended manner, to deal with potentially entirely novel environments.
However, decentralized adaptation poses an important scientific problem as nothing guarantees that adaptive mechanisms taking place at the individual level lead to the establishment of behaviors that are adaptive at the collective level: collective adaptation does not always follow from individual adaptation. First of all, many collectively efficient behaviors require the coordinated action of several individuals, and cannot be reached by the independent improvement of each individual separately. Second, and worst, individual adaptations can even harm, rather than help, collective efficiency if individuals have externalities for each other (e.g. if they enter into competition with one another, if they can underinvest into a collective good, etc.), in which case decentralized individual adaptation shall lead to a reduction of performance of the entire system.
Ever since Darwin, evolutionary biologists have acknowledged and studied this problem [1]. They have proposed various mechanisms through which collectively efficient outcomes (so-called cooperative outcomes) can be reached via individual adaptation [2-6]. Identifying the conditions in which individual adaptation can, or cannot, generate collectively efficient outcomes is indeed important to understand the origin of cooperation in biology, but it is also key to the design of practical solutions for open-ended adaptation in collective artificial systems such as swarm robotics [7,8].
The objective of this Master 2 internship will be to explore one important potential solution to this problem known from evolutionary biology: the role of reputation [9-11]. We will explore whether socially efficient outcomes are more easily reached by individual adaptation, when individuals can recognize others and are informed of their past behavior (their reputation). To this end, we will use evolutionary robotics [12], that is: the artificial evolution of embodied agents, in order to provide an accurate simulation of interactions between individuals.
# Administrative information
The internship will be done at UPMC, at the Institut des Systèmes Intelligents et de Robotique (ISIR), starting Feb. 1st for 6 months (internship gratification is approx. 500 euros per month). Co-advisors are: Nicolas Bredeche (ISIR/UPMC), Jean-Baptiste André (ENS)
# Short bibliography:
[1] Williams, G. C. (1966). Adaptation and natural selection: a critique of some current evolutionary thought. book, Princeton University Press.
[2] Hamilton, W. D. (1964). The genetical evolution of social behaviour, I & II. J Theor Biol, 7, 1–52.
[3] Axelrod, R., & Hamilton, W. D. (1981). The evolution of cooperation. Science, 211(4489), 1390–1396. article.
[4] Leimar, O., & Hammerstein, P. (2010). Cooperation for direct fitness benefits. Philosophical Transactions of the Royal Society B-Biological Sciences, 365(1553), 2619–2626.
[5] Bernard, A., André, J.-B., & Bredeche, N. (2016). To Cooperate or Not to Cooperate: Why Behavioural Mechanisms Matter. PLOS Computational Biology, 12(5), e1004886.
[6] Skyrms, B. (2004). The stag hunt and the evolution of social structure. book, Cambridge: Cambridge University Press.
[7] Bayindir, L., & Sahin, E. (2007) A Review of Studies in Swarm Robotics. Turk J Elec Engin, 15(2), 115–147.
[8] Rubenstein, M., Cornejo, A., & Nagpal, R. (2014) Programmable self-assembly in a thousand-robot swarm. Science, 345(6198), 795–799.
[9] Trivers, R. L. (1971). The evolution of reciprocal altruism. Quarterly Review of Biology, 46, 35–57. article.
[10] Nowak, M. A., & Sigmund, K. (1998). Evolution of indirect reciprocity by image scoring. Nature, 393(6685), 573–577. article.
[11] André, J.-B. (2010). The evolution of reciprocity: social types or social incentives? The American Naturalist, 175(2), 197–210.
[12] Doncieux S., Bredeche N., Mouret J.-B., Eiben A.E. Evolutionary Robotics: What, Why, and Where to. Frontiers in Robotics and AI, Volume 2, number 4, 2015.