Multiutility representations for incomplete difference preorders

A ``difference preorder'' is a (possibly incomplete) preorder on a space of state changes (rather than the states themselves); it encodes information about preference intensity, in addition to ordinal preferences. We find necessary and sufficient conditions for a difference preorder to be representable by a family of cardinal utility functions which take values in linearly ordered abelian groups. This has applications to interpersonal comparisons, social welfare, and decisions under uncertainty

Multiutility representations for incomplete difference preorders

A ``difference preorder'' is a (possibly incomplete) preorder on a space of state changes (rather than the states themselves); it encodes information about preference intensity, in addition to ordinal preferences. We find necessary and sufficient conditions for a difference preorder to be representable by a family of cardinal utility functions which take values in linearly ordered abelian groups. This has applications to interpersonal comparisons, social welfare, and decisions under uncertainty

Risky social choice with approximate interpersonal comparisons of well-being

We develop a model of social choice over lotteries, where people's psychological characteristics are mutable, their preferences may be incomplete, and approximate interpersonal comparisons of well-being are possible. Formally, we suppose individual preferences are described by a von~Neumann-Morgenstern (vNM) preference order on a space of lotteries over psychophysical states; the social planner must construct a vNM preference order on lotteries over social states. First we consider a model when the individual vNM preference order is incomplete (so not all interpersonal comparisons are possible). Then we consider a model where the individual vNM preference order is complete, but unknown to the planner, and thus modeled by a random variable. In both cases, we obtain characterizations of a utilitarian social welfare function

Social choice with approximate interpersonal comparison of welfare gains

Suppose it is possible to make approximate interpersonal comparisons of welfare gains and losses. Thus, if w, x, y, and z are personal psychophysical states (each encoding all ethically relevant information about the physical and mental state of a person), then it sometimes possible to say, ``The welfare gain of the state change w --> x is greater than the welfare gain of the state change y --> z.'' We can represent this by the formula ``(w --> x)> (y --> z)'', where `>' is a `difference preorder': an incomplete preorder on the space of all possible personal state changes. A `social state change' is a bundle of personal state changes. A `social difference preorder' (SDP) is an incomplete preorder on the space of social state changes, which satisfies Pareto and Anonymity axioms. The `minimal' SDP is the natural extension of the Suppes-Sen preorder to this setting; we show that it is a subrelation of every other SDP. The `approximate utilitarian' SDP ranks social state changes by comparing the sum total utility gain they induce, with respect to all `utility functions' compatible with `>'. The `net gain' preorder ranks social state changes by comparing the aggregate welfare gain they induce upon various subpopulations. We show that, under certain conditions, all three of these preorders coincide

Aggregation of incomplete ordinal preferences with approximate interpersonal comparisons

We develop a model of preference aggregation where people's psychological characteristics are mutable (hence, potential objects of individual or social choice), their preferences may be incomplete, and approximate interpersonal comparisons of well-being are possible. Formally, we consider preference aggregation when individual preferences are described by an incomplete, yet interpersonally comparable, preference order on a space of psychophysical states. Within this framework we characterize three preference aggregators: the `Suppes-Sen' preorder, the `approximate maximin' preorder, and the `approximate leximin' preorder

Risky social choice with approximate interpersonal comparisons of well-being

We develop a model of social choice over lotteries, where people's psychological characteristics are mutable, their preferences may be incomplete, and approximate interpersonal comparisons of well-being are possible. Formally, we suppose individual preferences are described by a von~Neumann-Morgenstern (vNM) preference order on a space of lotteries over psychophysical states; the social planner must construct a vNM preference order on lotteries over social states. First we consider a model when the individual vNM preference order is incomplete (so not all interpersonal comparisons are possible). Then we consider a model where the individual vNM preference order is complete, but unknown to the planner, and thus modeled by a random variable. In both cases, we obtain characterizations of a utilitarian social welfare function

Robust Statistical Comparison of Random Variables with Locally Varying Scale of Measurement

Spaces with locally varying scale of measurement, like multidimensional structures with differently scaled dimensions, are pretty common in statistics and machine learning. Nevertheless, it is still understood as an open question how to exploit the entire information encoded in them properly. We address this problem by considering an order based on (sets of) expectations of random variables mapping into such non-standard spaces. This order contains stochastic dominance and expectation order as extreme cases when no, or respectively perfect, cardinal structure is given. We derive a (regularized) statistical test for our proposed generalized stochastic dominance (GSD) order, operationalize it by linear optimization, and robustify it by imprecise probability models. Our findings are illustrated with data from multidimensional poverty measurement, finance, and medicine.Comment: Accepted for the 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023

Concepts for Decision Making under Severe Uncertainty with Partial Ordinal and Partial Cardinal Preferences

We introduce three different approaches for decision making under uncertainty if (I) there is only partial (both cardinally and ordinally scaled) information on an agent’s preferences and (II) the uncertainty about the states of nature is described by a credal set (or some other imprecise probabilistic model). Particularly, situation (I) is modeled by a pair of binary relations, one specifying the partial rank order of the alternatives and the other modeling partial information on the strength of preference. Our first approach relies on decision criteria constructing complete rankings of the available acts that are based on generalized expectation intervals. Subsequently, we introduce different concepts of global admissibility that construct partial orders between the available acts by comparing them all simultaneously. Finally, we define criteria induced by suitable binary relations on the set of acts and, therefore, can be understood as concepts of local admissibility. For certain criteria, we provide linear programming based algorithms for checking optimality/admissibility of acts. Additionally, the paper includes a discussion of a prototypical situation by means of a toy example