Power in Cultural Evolution and the Spread of Prosocial Norms

According to cultural evolutionary theory in the tradition of Boyd and Richerson, cultural evolution is driven by individuals' learning biases, natural selection, and random forces. Learning biases lead people to preferentially acquire cultural variants with certain contents or in certain contexts. Natural selection favors individuals or groups with fitness-promoting variants. Durham (1991) argued that Boyd and Richerson's approach is based on a "radical individualism" that fails to recognize that cultural variants are often "imposed" on people regardless of their individual decisions. Fracchia and Lewontin (2005) raised a similar challenge, suggesting that the success of a variant is often determined by the degree of power backing it. With power, a ruler can impose beliefs or practices on a whole population by diktat, rendering all of the forces represented in cultural evolutionary models irrelevant. It is argued here, based on work by Boehm (1999, 2012), that, from at least the time of the early Middle Paleolithic, human bands were controlled by powerful coalitions of the majority that deliberately guided the development of moral norms to promote the common good. Cultural evolutionary models of the evolution of morality have been based on false premises. However, Durham (1991) and Fracchia and Lewontin's (2005) challenge does not undermine cultural evolutionary modeling in nonmoral domains

Learning, Generalization, and Functional Entropy in Random Automata Networks

It has been shown \citep{broeck90:physicalreview,patarnello87:europhys} that feedforward Boolean networks can learn to perform specific simple tasks and generalize well if only a subset of the learning examples is provided for learning. Here, we extend this body of work and show experimentally that random Boolean networks (RBNs), where both the interconnections and the Boolean transfer functions are chosen at random initially, can be evolved by using a state-topology evolution to solve simple tasks. We measure the learning and generalization performance, investigate the influence of the average node connectivity $K$, the system size $N$, and introduce a new measure that allows to better describe the network's learning and generalization behavior. We show that the connectivity of the maximum entropy networks scales as a power-law of the system size $N$. Our results show that networks with higher average connectivity $K$ (supercritical) achieve higher memorization and partial generalization. However, near critical connectivity, the networks show a higher perfect generalization on the even-odd task

Adaptive Power Allocation and Control in Time-Varying Multi-Carrier MIMO Networks

In this paper, we examine the fundamental trade-off between radiated power and achieved throughput in wireless multi-carrier, multiple-input and multiple-output (MIMO) systems that vary with time in an unpredictable fashion (e.g. due to changes in the wireless medium or the users' QoS requirements). Contrary to the static/stationary channel regime, there is no optimal power allocation profile to target (either static or in the mean), so the system's users must adapt to changes in the environment "on the fly", without being able to predict the system's evolution ahead of time. In this dynamic context, we formulate the users' power/throughput trade-off as an online optimization problem and we provide a matrix exponential learning algorithm that leads to no regret - i.e. the proposed transmit policy is asymptotically optimal in hindsight, irrespective of how the system evolves over time. Furthermore, we also examine the robustness of the proposed algorithm under imperfect channel state information (CSI) and we show that it retains its regret minimization properties under very mild conditions on the measurement noise statistics. As a result, users are able to track the evolution of their individually optimum transmit profiles remarkably well, even under rapidly changing network conditions and high uncertainty. Our theoretical analysis is validated by extensive numerical simulations corresponding to a realistic network deployment and providing further insights in the practical implementation aspects of the proposed algorithm.Comment: 25 pages, 4 figure