Modeling scientific evidence: the challenge of specifying likelihoods

Evidence is an objective matter. This is the prevailing view within science, and confirmation theory should aim to capture the objective nature of scientific evidence. Modeling an objective evidence relation in a probabilistic framework faces two challenges: the probabilities must have the right epistemic foundation, and they must be specifiable given the hypotheses and data under consideration. Here I will explore how Sober's (2008, 2009) approach to confirmation handles these challenges of foundation and specification. In particular, I will argue that the specification problem proves especially difficult, and undermines the law of likelihood as an adequate representation of the objective nature of scientific evidence

Modeling scientific evidence: the challenge of specifying likelihoods

Evidence is an objective matter. This is the prevailing view within science, and confirmation theory should aim to capture the objective nature of scientific evidence. Modeling an objective evidence relation in a probabilistic framework faces two challenges: the probabilities must have the right epistemic foundation, and they must be specifiable given the hypotheses and data under consideration. Here I will explore how Sober's (2008, 2009) approach to confirmation handles these challenges of foundation and specification. In particular, I will argue that the specification problem proves especially difficult, and undermines the law of likelihood as an adequate representation of the objective nature of scientific evidence

Spineless and sentient: a challenge for moral comparison

We agree with Mikhalevich & Powell but take issue with their criteria for attributing sentience. This problem is connected with difficult issues concerning moral comparisons and evaluating moral decisions when interspecific moral interests conflict

Spontaneous emergence of groups and signaling diversity in dynamic networks

We study the coevolution of network structure and signaling behavior. We model agents who can preferentially associate with others in a dynamic network while they also learn to play a simple sender-receiver game. We have four major findings. First, signaling interactions in dynamic networks are sufficient to cause the endogenous formation of distinct signaling groups, even in an initially homogeneous population. Second, dynamic networks allow the emergence of novel {\em hybrid} signaling groups that do not converge on a single common signaling system but are instead composed of different yet complementary signaling strategies. We show that the presence of these hybrid groups promotes stable diversity in signaling among other groups in the population. Third, we find important distinctions in information processing capacity of different groups: hybrid groups diffuse information more quickly initially but at the cost of taking longer to reach all group members. Fourth, our findings pertain to all common interest signaling games, are robust across many parameters, and mitigate known problems of inefficient communication

Joint Agency and the Uniquely Human Cooperation Hypothesis

We propose an account of the evolution of joint agency that contrasts with views that take joint agency to be a uniquely human trait that facilitated the evolution of our social lifeway. We argue that there is huge variation in cooperative behavior and that while much human cooperative behavior may be explained by invoking cognitively rich capacities, such as joint intention, much cooperative behavior does not require such explanation. As a result, promising evolutionary approaches to cooperative behavior explain how it arises in many contexts. Our approach should also shed light on the evolution of such behavior in humans

Historical Reconstruction: Gaining Epistemic Access to the Deep Past

We discuss the scientific task of historical reconstruction and the problem of epistemic access. We argue that strong epistemic support for historical claims consists in the consilience of multiple independent lines of evidence, and analyze the impact hypothesis for the End-Cretaceous mass extinction to illustrate the accrual of epistemic support. Although there are elements of the impact hypothesis that enjoy strong epistemic support, the general conditions for this are strict, and help to clarify the difficulties associated with reconstructing the deep past

Manipulation and the Causes of Evolution

Evolutionary processes such as natural selection and random drift are commonly regarded as causes of population-level change. We respond to a recent challenge that drift and selection are best understood as statistical trends, not causes. Our reply appeals to manipulation as a strategy for uncovering causal relationships: if you can systematically manipulate variable A to bring about a change in variable B, then A is a cause of B. We argue that selection and drift can be systematically manipulated to produce different kinds of population-level change. They should therefore be regarded as causes