AUTHOR=Grigolini Paolo , Piccinini Nicola , Svenkeson Adam , Pramukkul Pensri , Lambert David , West Bruce J. TITLE=From Neural and Social Cooperation to the Global Emergence of Cognition JOURNAL=Frontiers in Bioengineering and Biotechnology VOLUME=3 YEAR=2015 URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2015.00078 DOI=10.3389/fbioe.2015.00078 ISSN=2296-4185 ABSTRACT=

The recent article (Turalska et al., 2012) discusses the emergence of intelligence via criticality as a consequence of locality breakdown. Herein, we use criticality for the foundation of a novel generation of game theory making the local interaction between players yield long-range effects. We first establish that criticality is not confined to the Ising-like structure of the sociological model of (Turalska et al., 2012), called the decision making model (DMM), through the study of the emergence of altruism using the altruism-selfishness model (ASM). Both models generate criticality, one by imitation of opinion (DMM) and the other by imitation of behavior (ASM). The dynamics of a sociological network š¯’® influences the behavioral network ā„± through two game theoretic paradigms: (i) the value of altruism; (ii) the benefit of rapid consensus. In (i), the network š¯’® debates the moral issue of altruism by means of the DMM, while at the level ā„± the individuals operate according to the ASM. The individuals of the level š¯’®, through a weak influence on the individuals of the level ā„±, exert a societal control on ā„±, fitting the principle of complexity management and complexity matching. In (ii), the benefit to society is the rapid attainment of consensus in the š¯’® level. The agents of the level ā„± operate according to the prisonerā€™s dilemma prescription, with the defectors acting as DMM contrarians at the level š¯’®. The contrarians, acting as the inhibitory links of neural networks, exert on society the same beneficial effect of maintaining the criticality-induced resilience that they generate in neural networks. The conflict between personal and social benefit makes the networks evolve toward criticality. Finally, we show that the theory of this article is compatible with recent discoveries in the burgeoning field of social neuroscience.