Automated Inference and the Future of Econometrics: A comment

This note discusses the (dis-)similarities between automated inference and computer-aided decisions, at the interface of econometrics and economics. It is argued that computer-aided decisions are best suited for scienti?c communication. For the future, the topic of learning is singled out as one of the most promising area of integration of econometric techniques and economics.

Ratio-Scale Elicitation of Degrees of Support

During the last decade, the computational paradigms known as inflzcence diagrams and belief networks have become to dominate the diagnostic expert systems field. Using elaborate collections of nodes and arcs, these representations describe how propositions of interest interact with each other through a variety of causal and predictive links. The links are parameterized with inexact degrees of support, typically expressed as subjective conditional probabilities or likelihood ratios. To date, most of the research in this area has focused on developing efficient belief-revision calculi to support decision making under uncertainty. Taking a different perspective, this paper focuses on the inputs of these calculi, i.e. on the human-supplied degrees of support which provide the currency of the belief revision process. Traditional methods for eliciting subjective probability functions are of little use in rule-based settings, where propositions of interest represent causally related and mostly discrete random variables. We describe ratio-scale and graphical methods for (i) eliciting degrees of support from human experts in a credible manner, and (ii) transforming them into the conditional probabilities and likelihood-ratios required by standard belief revision algorithms. As a secondary contribution, the paper offers a new graphical justification to eigenvector techniques for smoothing subjective answers to pair-wise elicitation questions.Information Systems Working Papers Serie

A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering

Objective Chances in a Deterministic World

Determinism is the thesis that the state of the world at any time uniquely determines the state of the world at all future times. Our best scientific theories seem inconclusive as to whether our world is deterministic. Our world could very well be either partially or completely deterministic. But determinism is not as innocuous as it seems; the truth of determinism seems to come into conflict with many intuitive concepts. One such concept is objective chance. Our intuitive notions of objective chances are tied to the belief that events could have turned out differently than the way they actually occurred. Though many philosophers have declared that this conception of objective chance is incompatible with deterministic worlds, some have tried to provide accounts that render the two compatible. In this thesis I investigate what a theory of deterministic chance could be. Working within certain metaphysical constraints on chance, I craft out a new dispositional account of chance grounded in properties that objects have

Measuring evidence: a probabilistic approach to an extension of Belnap-Dunn Logic

This paper introduces the logic of evidence and truth LETF as an extension of the Belnap-Dunn four-valued logic F DE. LETF is a slightly modified version of the logic LETJ, presented in Carnielli and Rodrigues (2017). While LETJ is equipped only with a classicality operator ○, LETF is equipped with a non-classicality operator ● as well, dual to ○. Both LETF and LETJ are logics of formal inconsistency and undeterminedness in which the operator ○ recovers classical logic for propositions in its scope. Evidence is a notion weaker than truth in the sense that there may be evidence for a proposition α even if α is not true. As well as LETJ, LETF is able to express preservation of evidence and preservation of truth. The primary aim of this paper is to propose a probabilistic semantics for LETF where statements P(α) and P(○α) express, respectively, the amount of evidence available for α and the degree to which the evidence for α is expected to behave classically - or non-classically for P(●α). A probabilistic scenario is paracomplete when P(α) + P(¬α) 1, and in both cases, P(○α) < 1. If P(○α) = 1, or P (●α) = 0, classical probability is recovered for α. The proposition ○α ∨ ●α, a theorem of LETF , partitions what we call the information space, and thus allows us to obtain some new versions of known results of standard probability theor

Assessing climate model projections: state of the art and philosophical reflections

The present paper draws on climate science and the philosophy of science in order to evaluate climate-model-based approaches to assessing climate projections. We analyze the difficulties that arise in such assessment and outline criteria of adequacy for approaches to it. In addition, we offer a critical overview of the approaches used in the IPCC working group one fourth report, including the confidence building, Bayesian and likelihood approaches. Finally, we consider approaches that do not feature in the IPCC reports, including three approaches drawn from the philosophy of science. We find that all available approaches face substantial challenges, with IPCC approaches having as a primary source of difficulty their goal of providing probabilistic assessments