Altruistically Inclined?: The Behavioral Sciences, Evolutionary Theory, and the Origins of Reciprocity

Altruistically Inclined? examines the implications of recent research in the natural sciences for two important social scientific approaches to individual behavior: the economic/rational choice approach and the sociological/anthropological. It considers jointly two controversial and related ideas: the operation of group selection within early human evolutionary processes and the likelihood of modularity—domain-specific adaptations in our cognitive mechanisms and behavioral predispositions. Experimental research shows that people will often cooperate in one-shot prisoner\u27s dilemma (PD) games and reject positive offers in ultimatum games, contradicting commonly accepted notions of rationality. Upon first appearance, predispositions to behave in this fashion could not have been favored by natural selection operating only at the level of the individual organism. Emphasizing universal and variable features of human culture, developing research on how the brain functions, and refinements of thinking about levels of selection in evolutionary processes, Alexander J. Field argues that humans are born with the rudiments of a PD solution module—and differentially prepared to learn norms supportive of it. His emphasis on failure to harm, as opposed to the provision of affirmative assistance, as the empirically dominant form of altruistic behavior is also novel. The point of departure and principal point of reference is economics. But Altruistically Inclined? will interest a broad range of scholars in the social and behavioral sciences, natural scientists concerned with the implications of research and debates within their fields for the conduct of work elsewhere, and educated lay readers curious about essential features of human nature.https://scholarcommons.scu.edu/faculty_books/1325/thumbnail.jp

An Initial Framework Assessing the Safety of Complex Systems

Trabajo presentado en la Conference on Complex Systems, celebrada online del 7 al 11 de diciembre de 2020.Atmospheric blocking events, that is large-scale nearly stationary atmospheric pressure patterns, are often associated with extreme weather in the mid-latitudes, such as heat waves and cold spells which have significant consequences on ecosystems, human health and economy. The high impact of blocking events has motivated numerous studies. However, there is not yet a comprehensive theory explaining their onset, maintenance and decay and their numerical prediction remains a challenge. In recent years, a number of studies have successfully employed complex network descriptions of fluid transport to characterize dynamical patterns in geophysical flows. The aim of the current work is to investigate the potential of so called Lagrangian flow networks for the detection and perhaps forecasting of atmospheric blocking events. The network is constructed by associating nodes to regions of the atmosphere and establishing links based on the flux of material between these nodes during a given time interval. One can then use effective tools and metrics developed in the context of graph theory to explore the atmospheric flow properties. In particular, Ser-Giacomi et al. [1] showed how optimal paths in a Lagrangian flow network highlight distinctive circulation patterns associated with atmospheric blocking events. We extend these results by studying the behavior of selected network measures (such as degree, entropy and harmonic closeness centrality)at the onset of and during blocking situations, demonstrating their ability to trace the spatio-temporal characteristics of these events.This research was conducted as part of the CAFE (Climate Advanced Forecasting of sub-seasonal Extremes) Innovative Training Network which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 813844

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Interplay between network configurations and network governance mechanisms in supply networks a systematic literature review

Purpose: This work systematically reviews the extant academic management literature on supply networks. It specifically examines how network configurations and network governance mechanisms influence each other in supply networks. Design: 125 analytical and empirical studies were identified using an evidence-based approach to review the literature mainly published between 1985 and 2012. Synthesis: Drawing on a multi-disciplinary theoretical foundation, this work develops an integrative framework to identify three distinct yet interdependent themes that characterize the study of supply networks: a) Network Configurations (structures and relationships); b) Network Governance Mechanisms (formal and informal); and c) The Interplay between Network Configurations and Network Governance Mechanisms. Findings: Network configurations and network governance mechanisms mutually influence each other and cannot be considered in isolation. Formal and informal governance mechanisms provide better control when used as complements rather than as substitutes. The choice of governance mechanism depends on the nature of exchange; role of management; desired level of control; level of flexibility in formal contracts; and complementary role of formal and informal governance mechanism. Research implications: This nascent field has thematic and methodological research opportunities for academics. Comparative network analysis using longitudinal case studies offers a rich area for further study. Practical Implications: The complexity surrounding the conflicting roles of managers at the organisation and network levels poses a significant challenge during the development and implementation stage of strategic network policies. Originality/value: This review reveals that formal and informal governance mechanisms provide better control when used as complements rather than as substitutes

Task Allocation in Foraging Robot Swarms:The Role of Information Sharing

Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms