Optimized Weighted Nearest Neighbours Matching Algorithm for Control Group Selection

An essential criterion for the proper implementation of case-control studies is selecting appropriate case and control groups. In this article, a new simulated annealing-based control group selection method is proposed, which solves the problem of selecting individuals in the control group as a distance optimization task. The proposed algorithm pairs the individuals in the n-dimensional feature space by minimizing the weighted distances between them. The weights of the dimensions are based on the odds ratios calculated from the logistic regression model fitted on the variables describing the probability of membership of the treated group. For finding the optimal pairing of the individuals, simulated annealing is utilized. The effectiveness of the newly proposed Weighted Nearest Neighbours Control Group Selection with Simulated Annealing (WNNSA) algorithm is presented by two Monte Carlo studies. Results show that the WNNSA method can outperform the widely applied greedy propensity score matching method in feature spaces where only a few covariates characterize individuals and the covariates can only take a few values.

Why don’t cockatoos have war songs?

We suggest the accounts offered by the target articles could be strengthened by acknowledging the role of group selection and cultural niche construction in shaping the evolutionary trajectory of human music. We argue that group level traits and highly variable cultural niches can explain the diversity of human song, but the target articles’ accounts are insufficient to explain such diversity.

Social evolution as moral truth tracking in natural law

Morality can be adaptive or maladaptive. From this fact come polarizing disputes on the meta-ethical status of moral adaptation. The realist tracking account of morality claims that it is possible to track objective moral truths and that these truths correspond to moral rules that are adaptive. In contrast, evolutionary anti-realism rejects the existence of moral objectivity and thus asserts that adaptive moral rules cannot represent objective moral truths, since those truths do not exist. This article develops a novel evolutionary view of natural law to defend the realist tracking account. It argues that we can identify objective moral truths through cultural group selection and that adaptive moral rules are likely to reflect such truths.

Historical Origins

This chapter traces the origins of the gene’s-eye view through three sections of evolutionary biology. The first is adaptationism, the tradition that takes the appearance of design in living world to be the cardinal problem a theory of evolution needs to explain. The chapter shows how this view has been especially prominent in British biology, owing the strong standing of natural theology and the writings of William Paley. The second is the emergence of population genetics during the modern synthesis. Here, the work of R.A. Fisher was particularly important. The third and final section was the levels selection debate and the rejection of group selection. G.C. Williams led the way the way and the origin of the gene’s-eye view culminated with the publication of The Selfish Gene.

Social Evolution as Moral Truth Tracking in Natural Law

Morality can be adaptive or maladaptive. From this fact come polarising disputes on the meta-ethical status of moral adaptation. The realist tracking account of morality claims that it is possible to track objective moral truths and that these truths correspond to moral rules that are adaptive. In contrast, evolutionary anti-realism rejects the existence of moral objectivity and thus asserts that adaptive moral rules cannot represent objective moral truths, since those truths do not exist. This article develops a novel evolutionary view of natural law to defend the realist tracking account. It argues that we can identify objective moral truths via cultural group selection and that adaptive moral rules are likely to reflect such truths.

Performance Analysis of Group Selection in UAV Networks with Backhaul-Aware Biasing

The limited wireless backhaul capacity has become the major bottleneck for UAV communications, while in existing researches of UAV networks, the relation between cell selection and backhaul capacity has not been modeled. This paper proposes cell group selection with backhaul-aware biasing for UAV networks and analyzes system performance by deriving the rate outage probability via stochastic geometry, where the user’s maximum data rate is constrained by backhaul capacity. Specifically, cell group selection is no longer distance-based and considered with backhaul capacity bias factor, where UAVs with higher backhaul capacity will have a larger bias factor to match the backhaul variance. In addition, the dynamic UAV group is organized with the N largest bias reference signal receiving power (BRSRP), where users can utilize the diversity gain by adjusting serving UAV dynamically as the channel conditions change. Analytical results show that the outage probability is decreased by 58% when cell group size N = 3 and UAV optimal density λ u = 600 / k m 2 when UAV height h = 150 m .