Bayesian fairness

We consider the problem of how decision making can be fair when the underlying probabilistic model of the world is not known with certainty. We argue that recent notions of fairness in machine learning need to explicitly incorporate parameter uncertainty, hence we introduce the notion of {\em Bayesian fairness} as a suitable candidate for fair decision rules. Using balance, a definition of fairness introduced by Kleinberg et al (2016), we show how a Bayesian perspective can lead to well-performing, fair decision rules even under high uncertainty.Comment: 13 pages, 8 figures, to appear at AAAI 201

Looking for a psychology for the inner rational agent

Research in psychology and behavioural economics shows that individuals’ choices often depend on ‘irrelevant’ contextual factors. This presents problems for normative economics, which has traditionally used preference-satisfaction as its criterion. A common response is to claim that individuals have context-independent latent preferences which are ‘distorted’ by psychological factors, and that latent preferences should be respected. This response implicitly uses a model of human action in which each human being has an ‘inner rational agent’. I argue that this model is psychologically ungrounded. Although references to latent preferences appear in psychologically-based explanations of context-dependent choice, latent preferences serve no explanatory purpose

Traveller Behaviour: Decision making in an unpredictable world

This paper discusses the nature and consequences of uncertainty in transport systems. Drawing on work from a number of fields, it addresses travellers’ abilities to predict variable phenomena, their perception of uncertainty, their attitude to risk and the various strategies they might adopt in response to uncertainty. It is argued that despite the increased interest in the representation of uncertainty in transport systems, most models treat uncertainty as a purely statistical issue and ignore the psychological aspects of response to uncertainty. The principle theories and models currently used to predict travellers’ response to uncertainty are presented and number of alternative modelling approaches are outlined. It is argued that the current generation of predictive models do not provide an adequate basis for forecasting response to changes in the degree of uncertainty or for predicting the likely effect of providing additional information. A number of alternative modelling approaches are identified to deal with travellers’ acquisition of information, the definition of their choice set and their choice between the available options. The use of heuristic approaches is recommended as an alternative to more conventional probabilistic methods