Evidential Decision Theory

Evidential Decision Theory is a radical theory of rational decision-making. It recommends that instead of thinking about what your decisions *cause*, you should think about what they *reveal*. This Element explains in simple terms why thinking in this way makes a big difference, and argues that doing so makes for *better* decisions. An appendix gives an intuitive explanation of the measure-theoretic foundations of Evidential Decision Theory.

Newcomb dilemma in development management

Newcomb dilemmas show a discrepancy in our rational reasoning, as made clear by comparing Evidential Decision Theory with Causal Decision Theory. In this paper, I look at three versions of the dilemma: the original, highly technical and abstract one plus two more mundane cases. I also account for the general schema of the dilemma possibly appearing in macroeconomic situations. Ahmed (2014) aims to provide a solution for macroeconomic cases that opens room for forming a development management Newcomb dilemma – an imaginary case of electric motor competition between Toyota and Tesla. I argue that Ahmed’s solution may solve the macroeconomic Newcomb dilemma, but it cannot be applied to the development management dilemma. If I am right, similar Newcomb situations could be cropping up regularly in development management, leading to seemingly insoluble strategic decisions having to be made. This may create an inevitable pitfall for development management.

Agency, Intervention, and the Past

Although agency theories of causation are unsuccessful, they draw on two plausible contributions to the analysis of causation: a characterization of agent non-symmetry in terms of effective means and an insight into nature of the similarity weighting for counterfactuals. A development of evidential decision theory provides the most immediately plausible way of understanding agency asymmetry. However, a problem with the proposed development reveals the importance of causal thinking—captured in causal decision theory—in characterizing when an action fails to be the most effective means to a certain end. Non-reductive interventionist approaches to causation are unnecessary because the recommended similarity weighting captures the appropriate notion of intervention. The recommended approach to causal non-symmetry can explain the fact that, metaphysically necessarily, causes usually precede their effect because temporal direction is preponderant causal direction. The non-symmetry of agency is related to two de facto asymmetries relating to knowledge and intervention.

Cheating Death in Damascus

Evidential Decision Theory (EDT) and Causal Decision Theory (CDT) are the leading contenders as theories of rational action, but both face counterexamples. We present some new counterexamples, including one in which the optimal action is causally dominated. We also present a novel decision theory, Functional Decision Theory (FDT), which simultaneously solves both sets of counterexamples. Instead of considering which physical action of theirs would give rise to the best outcomes, FDT agents consider which output of their decision function would give rise to the best outcome. This theory relies on a notion of subjunctive dependence, where multiple implementations of the same mathematical function are considered (even counterfactually) to have identical results for logical rather than causal reasons. Taking these subjunctive dependencies into account allows FDT agents to outperform CDT and EDT agents in, for example, the presence of accurate predictors.

Equal Opportunities in Newcomb’s Problem and Elsewhere

The paper discusses Ian Wells’s recent argument (Wells 2019) that there is a decision problem in which followers of Evidential Decision Theory end up poorer than followers of Causal Decision Theory despite having the same opportunities for money. It defends Evidential Decision Theory against Wells’s argument, on the following grounds. (i) Wells's has not presented a decision problem in which his main claim is true. (ii) Four possible decision problems can be generated from his central example, in each of which followers of Evidential Decision Theory do at least as well as followers of Causal Decision Theory (but the former typically have better opportunities for money). (iii) There is another case in which followers of Causal Decision Theory have the same opportunities for making money but end up worse than followers of Evidential Decision Theory.

Newcomb University: A play in one act

Counter-intuitive consequences of both causal decision theory and evidential decision theory are dramatized. Each of those theories is thereby put under some pressure to supply an error theory to explain away intuitions that seem to favour the other. Because trouble is stirred up for both sides, complacency about Newcomb’s problem is discouraged.

Objective Value Is Always Newcombizable

This paper argues that evidential decision theory is incompatible with options having objective values. If options have objective values, then it should always be rationally permissible for an agent to choose an option if they are certain that the option uniquely maximizes objective value. But, as we show, if options have objective values and evidential decision theory is true, then it is not always rationally permissible for an agent to choose an option if they are certain that the option uniquely maximizes objective value.

Evidential Decision Tree Based on Belief Entropy

Decision Tree is widely applied in many areas, such as classification and recognition. Traditional information entropy and Pearson’s correlation coefficient are often applied as measures of splitting rules to find the best splitting attribute. However, these methods can not handle uncertainty, since the relation between attributes and the degree of disorder of attributes can not be measured by them. Motivated by the idea of Deng Entropy, it can measure the uncertain degree of Basic Belief Assignment (BBA) in terms of uncertain problems. In this paper, Deng entropy is used as a measure of splitting rules to construct an evidential decision tree for fuzzy dataset classification. Compared to traditional combination rules used for combination of BBAs, the evidential decision tree can be applied to classification directly, which efficiently reduces the complexity of the algorithm. In addition, the experiments are conducted on iris dataset to build an evidential decision tree that achieves the goal of more accurate classification.