Statistical Physics of Evolutionary Trajectories on Fitness Landscapes

Random walks on multidimensional nonlinear landscapes are of interest in many areas of science and engineering. In particular, properties of adaptive trajectories on fitness landscapes determine population fates and thus play a central role in evolutionary theory. The topography of fitness landscapes and its effect on evolutionary dynamics have been extensively studied in the literature. We will survey the current research knowledge in this field, focusing on a recently developed systematic approach to characterizing path lengths, mean first-passage times, and other statistics of the path ensemble. This approach, based on general techniques from statistical physics, is applicable to landscapes of arbitrary complexity and structure. It is especially well-suited to quantifying the diversity of stochastic trajectories and repeatability of evolutionary events. We demonstrate this methodology using a biophysical model of protein evolution that describes how proteins maintain stability while evolving new functions

Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability

Post-hoc model-agnostic interpretation methods such as partial dependence plots can be employed to interpret complex machine learning models. While these interpretation methods can be applied regardless of model complexity, they can produce misleading and verbose results if the model is too complex, especially w.r.t. feature interactions. To quantify the complexity of arbitrary machine learning models, we propose model-agnostic complexity measures based on functional decomposition: number of features used, interaction strength and main effect complexity. We show that post-hoc interpretation of models that minimize the three measures is more reliable and compact. Furthermore, we demonstrate the application of these measures in a multi-objective optimization approach which simultaneously minimizes loss and complexity

Measuring Symbol and Icon Characteristics: Norms for Concreteness, Complexity, Meaningfulness, Familiarity, and Semantic Distance for 239 Symbols

This paper provides rating norms for a set of symbols and icons selected from a wide variety of sources. These ratings enable the effects of symbol characteristics on user performance to be systematically investigated. The symbol characteristics that have been quantified are considered to be of central relevance to symbol usability research and include concreteness, complexity, meaningfulness, familiarity, and semantic distance. The interrelationships between each of these dimensions is examined and the importance of using normative ratings for experimental research is discussed

Computing the Unique Information

Given a pair of predictor variables and a response variable, how much information do the predictors have about the response, and how is this information distributed between unique, redundant, and synergistic components? Recent work has proposed to quantify the unique component of the decomposition as the minimum value of the conditional mutual information over a constrained set of information channels. We present an efficient iterative divergence minimization algorithm to solve this optimization problem with convergence guarantees and evaluate its performance against other techniques.Comment: To appear in 2018 IEEE International Symposium on Information Theory (ISIT); 18 pages; 4 figures, 1 Table; Github link to source code: https://github.com/infodeco/computeU

Towards informed and multi-faceted wildlife trade interventions

International trade in wildlife is a key threat to biodiversity conservation. CITES, the Convention on International Trade in Endangered Species of Wild Fauna and Flora, is the primary mechanism for controlling international wildlife trade and seeks to ensure it is sustainable, relying on trade bans and controls. However, there has been little comprehensive review of the effectiveness of CITES. Here, we review typical and atypical approaches taken to regulate wildlife trade in CITES and assert that it boasts few successes. We attribute this to: non-compliance, an over reliance on regulation, lack of knowledge of listed species, ignorance of the reality of market forces, and influence among CITES actors. To more effectively manage trade we argue that interventions need to go beyond regulation and should be multi-faceted, reflecting the complexity of wildlife trade. To inform such interventions we assert an intensive research effort is needed and we outline six key research areas: (1) factors undermining wildlife trade governance at the national level, (2) determining sustainable harvest rates for CITES species, (3) gaining the buy-in of local communities in implementing CITES, (4) supply and demand based market interventions, (5) means of quantifying illicit trade, and (6) political processes and influence within CITES