Influence-Optimistic Local Values for Multiagent Planning --- Extended Version

Recent years have seen the development of methods for multiagent planning under uncertainty that scale to tens or even hundreds of agents. However, most of these methods either make restrictive assumptions on the problem domain, or provide approximate solutions without any guarantees on quality. Methods in the former category typically build on heuristic search using upper bounds on the value function. Unfortunately, no techniques exist to compute such upper bounds for problems with non-factored value functions. To allow for meaningful benchmarking through measurable quality guarantees on a very general class of problems, this paper introduces a family of influence-optimistic upper bounds for factored decentralized partially observable Markov decision processes (Dec-POMDPs) that do not have factored value functions. Intuitively, we derive bounds on very large multiagent planning problems by subdividing them in sub-problems, and at each of these sub-problems making optimistic assumptions with respect to the influence that will be exerted by the rest of the system. We numerically compare the different upper bounds and demonstrate how we can achieve a non-trivial guarantee that a heuristic solution for problems with hundreds of agents is close to optimal. Furthermore, we provide evidence that the upper bounds may improve the effectiveness of heuristic influence search, and discuss further potential applications to multiagent planning.Comment: Long version of IJCAI 2015 paper (and extended abstract at AAMAS 2015

A comparison of reliability coefficients for psychometric tests that consist of two parts

If a test consists of two parts the Spearman-Brown formula and Flanagan's coefficient (Cronbach's alpha) are standard tools for estimating the reliability. However, the coefficients may be inappropriate if their associated measurement models fail to hold. We study the robustness of reliability estimation in the two-part case to coefficient misspecification. We compare five reliability coefficients and study various conditions on the standard deviations and lengths of the parts. Various conditional upper bounds of the differences between the coefficients are derived. It is shown that the difference between the Spearman-Brown formula and Horst's formula is negligible in many cases. We conclude that all five reliability coefficients can be used if there are only small or moderate differences between the standard deviations and the lengths of the parts

Additive kappa can be increased by combining adjacent categories

Weighted kappa is a measure that is commonly used for quantifying similarity between two ordinal variables with identical categories. Additive kappa is a special case of weighted kappa that allows the researcher to specify distances between adjacent categories. It is shown that additive kappa is a weighted average of the additive kappas of all collapsed tables of a specific size. It follows that, if the reliability of a categorical rating instrument is assessed with additive kappa, the reliability can be increased by combining categories.<br/

Kappa coefficients for dichotomous-nominal classifications

Two types of nominal classifications are distinguished, namely regular nominal classifications and dichotomous-nominal classifications. The first type does not include an 'absence' category (for example, no disorder), whereas the second type does include an 'absence' category. Cohen's unweighted kappa can be used to quantify agreement between two regular nominal classifications with the same categories, but there are no coefficients for assessing agreement between two dichotomous-nominal classifications. Kappa coefficients for dichotomous-nominal classifications with identical categories are defined. All coefficients proposed belong to a one-parameter family. It is studied how the coefficients for dichotomous-nominal classifications are related and if the values of the coefficients depend on the number of categories. It turns out that the values of the new kappa coefficients can be strictly ordered in precisely two ways. The orderings suggest that the new coefficients are measuring the same thing, but to a different extent. If one accepts the use of magnitude guidelines, it is recommended to use stricter criteria for the new coefficients that tend to produce higher values

Changing Choices

Changing choices psychological relativity theory unifying theory transformation parameters psychology psygologie koornstra choice dynamics The book contains a unifying theory on how the common object space is metrically transformed by individuals with different transformation parameters, due to their other previous experiences, to individually different psychological spaces for judgment on the one hand and preference on the other hand. Individual experiences also change generally, whereby the psychological spaces also change generally for each individual. The theory, therefore, is a psychological relativity theory of perception, judgment, preference, and choice dynamics. This book is a must read for all behavioural, economic, and social scientists with theoretical interest and some understanding of multidimensional data analyses. It integrates more than twenty theories on perception, judgment, preference, and risk decisions into one mathematical theory. Knowledge of advanced mathematics and modern geometry is not needed, because the mathematical subsections can be skipped without loss of understanding, due to their explanation and illustration by figures in the text