Simple trees in complex forests: Growing Take The Best by Approximate Bayesian Computation

How can heuristic strategies emerge from smaller building blocks? We propose Approximate Bayesian Computation as a computational solution to this problem. As a first proof of concept, we demonstrate how a heuristic decision strategy such as Take The Best (TTB) can be learned from smaller, probabilistically updated building blocks. Based on a self-reinforcing sampling scheme, different building blocks are combined and, over time, tree-like non-compensatory heuristics emerge. This new algorithm, coined Approximately Bayesian Computed Take The Best (ABC-TTB), is able to recover a data set that was generated by TTB, leads to sensible inferences about cue importance and cue directions, can outperform traditional TTB, and allows to trade-off performance and computational effort explicitly

Are Reasons Causally Relevant for Action? Dharmakīrti and the Embodied Cognition Paradigm

How do mental states come to be about something other than their own operations, and thus to serve as ground for effective action? This papers argues that causation in the mental domain should be understood to function on principles of intelligibility (that is, on principles which make it perfectly intelligible for intentions to have a causal role in initiating behavior) rather than on principles of mechanism (that is, on principles which explain how causation works in the physical domain). The paper considers Dharmakīrti’s kāryānumāna argument (that is, the argument that an inference is sound only when one infers from the effect to the cause and not vice versa), and proposes a naturalized account of reasons. On this account, careful scrutiny of the effect can provide a basis for ascertaining the unique causal totality that is its source, but only for reasoning that is context‐specific

Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena

Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is further complicated by many theoretical issues, such as the I-equivalence among different structures. In this work, we focus on a specific subclass of BNs, named Suppes-Bayes Causal Networks (SBCNs), which include specific structural constraints based on Suppes' probabilistic causation to efficiently model cumulative phenomena. Here we compare the performance, via extensive simulations, of various state-of-the-art search strategies, such as local search techniques and Genetic Algorithms, as well as of distinct regularization methods. The assessment is performed on a large number of simulated datasets from topologies with distinct levels of complexity, various sample size and different rates of errors in the data. Among the main results, we show that the introduction of Suppes' constraints dramatically improve the inference accuracy, by reducing the solution space and providing a temporal ordering on the variables. We also report on trade-offs among different search techniques that can be efficiently employed in distinct experimental settings. This manuscript is an extended version of the paper "Structural Learning of Probabilistic Graphical Models of Cumulative Phenomena" presented at the 2018 International Conference on Computational Science