Modeling cooperation among self-interested agents: a critique

This paper reviews recent attempts to model cooperation in groups of self-interested unrelated individuals. It concludes that no empirically plausible model has been developed, and the economist's quest for an explanation of human prosociality in terms of self-interested agents has not been successful. We show that the equilibrium criteria employed in standard game-theoretic models have no attractive dynamic properties, and the mechanisms deployed in these models are not generally seen in empirical examples of cooperative groups. We also show that these models are not robust to plausible rates of signaling error and mutation. We present an alternative, involving strongly reciprocal agents, that satisfies all the requirements for a dynamically stable model of cooperation.     

Charisma as signal: An evolutionary perspective on charismatic leadership

We present an evolutionary perspective on charismatic leadership, arguing that charisma has evolved as a credible signal of a person's ability to solve a coordination challenge requiring urgent collective action from group members. We suggest that a better understanding of charisma's evolutionary and biological origins and functions can provide a broader perspective in which to situate current debates surrounding the utility and validity of charismatic leadership as a construct in the social sciences. We outline several key challenges which have shaped our followership psychology, and argue that the benefits of successful coordination in ancestral environments has led to the evolution of context-dependent psychological mechanisms which are especially attuned to cues and signals of outstanding personal leadership qualities. We elaborate on several implications of this signaling hypothesis of charismatic leadership, including opportunities for deception (dishonest signaling) and for large-scale coordination.     

Bayesian optimal sequential design for nonparametric regression via inhomogeneous evolutionary MCMC

We develop a novel computational methodology for Bayesian optimal sequential design for nonparametric regression. This computational methodology, that we call inhomogeneous evolutionary Markov chain Monte Carlo, combines ideas of simulated annealing, genetic or evolutionary algorithms, and Markov chain Monte Carlo. Our framework allows optimality criteria with general utility functions and general classes of priors for the underlying regression function. We illustrate the usefulness of our novel methodology with applications to experimental design for nonparametric function estimation using Gaussian process priors and free-knot cubic splines priors.     

共享单车用户机会主义行为的演化博弈分析

目前为止,共享单车用户的机会主义行为是共享单车能否发展的关键制约因素之一。基于此,尝试用演化博弈理论建立共享单车企业与用户间的演化博弈模型发掘用户的机会主义行为动因及利益相关者作用。研究表明:用户收益是其行为选择的决定性因素,用户机会主义行为收益大于规范行为收益是机会主义行为的产生根源;共享单车企业的激励约束可对用户行为起到调节作用,约束强度将对用户机会主义行为的程度产生负向影响。最后,针对演化博弈模型分析结果,提出了相应管理对策建议。     

售电侧改革、市场主体变化与电价红利:基于讨价还价博弈

本文通过构建“一对多”和“多对多”讨价还价博弈模型,研究了售电侧改革前后,发电商和售电公司之间关于消费者剩余分配的博弈过程。具体来说,文章从售电侧改革带来的博弈主体与博弈机制变化角度,揭示了售电侧改革对用户电价红利的影响。研究发现:售电侧放开后,电力市场交易机制由“一对多”竞价上网变为“多对多”讨价还价匹配,造成了售电侧博弈主体的议价能力下降,发电侧博弈主体相对议价能力上升。发电商群体将索取更多的消费者剩余,从而推动发电侧整体报价的上涨,挤压了售电侧的利润空间,导致售电公司被迫抬高市场电价,最终剥夺了用户的电价红利。研究揭示了售电侧改革未能带来电价下降的根本原因。     

‘We swam before we breathed or walked’: able-bodied belonging in popular stories of evolutionary biology

This article introduces the concept of ‘able-bodied belonging,’ and pays particular attention to the cultural mechanisms in which ableism intertwines with the forms of belonging. Taking a cultural studies viewpoint, the article focuses on present evolutionary biological accounts, and explores the ableist and speciesist assumptions that frame evolutionary biology. The article investigates how these accounts invoke a feeling of belonging to the animal world in ways which reinforce the idea that only a particular kind of body is species-typical of humans. First, the article explores how the cultural stories that emphasize the connection between human beings and non-human species eventually distance particular bodies from humanity. Second, the article shows how humans’ connection to the animal world could emerge in a way that contests the exceptionality of able-bodied humanity.     

Evaluation of multi-objective optimization approaches for solving green supply chain design problems

This paper evaluates the applicability of different multi-objective optimization methods for environmentally conscious supply chain design. We analyze a case study with three objectives: costs, CO₂ and fine dust (also known as PM – Particulate Matters) emissions. We approximate the Pareto front using the weighted sum and epsilon constraint scalarization methods with pre-defined or adaptively selected parameters, two popular evolutionary algorithms, SPEA2 and NSGA-II, with different selection strategies, and their interactive counterparts that incorporate Decision Maker׳s (DM׳s) indirect preferences into the search process. Within this case study, the CO₂ emissions could be lowered significantly by accepting a marginal increase of costs over their global minimum. NSGA-II and SPEA2 enabled faster estimation of the Pareto front, but produced significantly worse solutions than the exact optimization methods. The interactive methods outperformed their a posteriori counterparts, and could discover solutions corresponding better to the DM preferences. In addition, by adjusting appropriately the elicitation interval and starting generation of the elicitation, the number of pairwise comparisons needed by the interactive evolutionary methods to construct a satisfactory solution could be decreased.     

A model for collaboration networks giving rise to a power-law distribution with an exponential cutoff

Recently several authors have proposed stochastic evolutionary models for the growth of complex networks that give rise to power-law distributions. These models are based on the notion of preferential attachment leading to the “rich get richer” phenomenon. Despite the generality of the proposed stochastic models, there are still some unexplained phenomena, which may arise due to the limited size of networks such as protein, e-mail, actor and collaboration networks. Such networks may in fact exhibit an exponential cutoff in the power-law scaling, although this cutoff may only be observable in the tail of the distribution for extremely large networks. We propose a modification of the basic stochastic evolutionary model, so that after a node is chosen preferentially, say according to the number of its inlinks, there is a small probability that this node will become inactive. We show that as a result of this modification, by viewing the stochastic process in terms of an urn transfer model, we obtain a power-law distribution with an exponential cutoff. Unlike many other models, the current model can capture instances where the exponent of the distribution is less than or equal to two. As a proof of concept, we demonstrate the consistency of our model empirically by analysing the Mathematical Research collaboration network, the distribution of which has been shown to be compatible with a power law with an exponential cutoff.     

Clustering and combinatorial optimization in recursive supervised learning

The use of combinations of weak learners to learn a dataset has been shown to be better than the use of a single strong learner. In fact, the idea is so successful that boosting, an algorithm combining several weak learners for supervised learning, has been considered to be the best off the shelf classifier. However, some problems still exist, including determining the optimal number of weak learners and the over fitting of data. In an earlier work, we developed the RPHP algorithm which solves both these problems by using a combination of global search, weak learning and pattern distribution. In this chapter, we revise the global search component by replacing it with a cluster based combinatorial optimization. Patterns are clustered according to the output space of the problem, i.e., natural clusters are formed based on patterns belonging to each class. A combinatorial optimization problem is therefore created, which is solved using evolutionary algorithms. The evolutionary algorithms identify the “easy” and the “difficult” clusters in the system. The removal of the easy patterns then gives way to the focused learning of the more complicated patterns. The problem therefore becomes recursively simpler. Over fitting is overcome by using a set of validation patterns along with a pattern distributor. An algorithm is also proposed to use the pattern distributor to determine the optimal number of recursions and hence the optimal number of weak learners for the problem. Empirical studies show generally good performance when compared to other state of the art methods.     

后现代之后的信仰重构——E.L.多克特罗的《上帝之城》对奥古斯丁的背离与继承

美国当代犹太作家E.L.多克特罗于2000年出版了里程碑式的长篇小说《上帝之城》。在这部小说中,作者站在世纪之交的制高点上,对20世纪的"世纪病"——信仰问题,进行了全面深入的反思,并在批判继承奥古斯丁宗教思想的基础上,立足伦理道德,创造性地建构了"进化派犹太教",倡导一种现世的、当下的信仰。面对人类日益加剧的信仰问题,多克特罗以一个作家的责任心,在后现代文化的荒漠中指出了一条可能的出路。