An Empirical Assessment of Alternative Models of Risky Decision Making

In this paper, we assess the degree to which four of the most commonly used models of risky decision making can explain the choices individuals make when faced with risky prospects. To make this assessment, we use experimental evidence for two random samples of young adults. Using a robust, nonlinear least squares procedure, we estimate a model that is general enough to approximate Kahnenman and Tversky's prospect theory and that for certain parametric values will yield the expected utility model, a subjective expected utility model and a probability-transform model. We find that the four models considered explain the decision-making behavior of the majority of our subjects. Surprisingly, we find that the choice behavior of the largest number of subjects is consistent with a probability-transform model. Such models have only been developed recently and have not been used in applied settings. We find least support for the expected utility model -- the most widely used model of risky decision making.

On the Computability of Solomonoff Induction and Knowledge-Seeking

Solomonoff induction is held as a gold standard for learning, but it is known to be incomputable. We quantify its incomputability by placing various flavors of Solomonoff's prior M in the arithmetical hierarchy. We also derive computability bounds for knowledge-seeking agents, and give a limit-computable weakly asymptotically optimal reinforcement learning agent.Comment: ALT 201

Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities

The follow the leader (FTL) algorithm, perhaps the simplest of all online learning algorithms, is known to perform well when the loss functions it is used on are positively curved. In this paper we ask whether there are other “lucky” settings when FTL achieves sublinear, “small” regret. In particular, we study the fundamental problem of linear prediction over a non-empty convex, compact domain. Amongst other results, we prove that the curvature of the boundary of the domain can act as if the losses were curved: In this case, we prove that as long as the mean of the loss vectors have positive lengths bounded away from zero, FTL enjoys a logarithmic growth rate of regret, while, e.g., for polyhedral domains and stochastic data it enjoys finite expected regret. Building on a previously known meta-algorithm, we also get an algorithm that simultaneously enjoys the worst-case guarantees and the bound available for FTL

Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities

The follow the leader (FTL) algorithm, perhaps the simplest of all online learning algorithms, is known to perform well when the loss functions it is used on are positively curved. In this paper we ask whether there are other “lucky” settings when FTL achieves sublinear, “small” regret. In particular, we study the fundamental problem of linear prediction over a non-empty convex, compact domain. Amongst other results, we prove that the curvature of the boundary of the domain can act as if the losses were curved: In this case, we prove that as long as the mean of the loss vectors have positive lengths bounded away from zero, FTL enjoys a logarithmic growth rate of regret, while, e.g., for polyhedral domains and stochastic data it enjoys finite expected regret. Building on a previously known meta-algorithm, we also get an algorithm that simultaneously enjoys the worst-case guarantees and the bound available for FTL

Universal knowledge-seeking agents for stochastic environments

We define an optimal Bayesian knowledge-seeking agent, KL-KSA, designed for countable hypothesis classes of stochastic environments and whose goal is to gather as much information about the unknown world as possible. Although this agent works for arbitrary countable classes and priors, we focus on the especially interesting case where all stochastic computable environments are considered and the prior is based on Solomonoff’s universal prior. Among other properties, we show that KL-KSA learns the true environment in the sense that it learns to predict the consequences of actions it does not take. We show that it does not consider noise to be information and avoids taking actions leading to inescapable traps. We also present a variety of toy experiments demonstrating that KL-KSA behaves according to expectation

Extreme State Aggregation Beyond MDPs

We consider a Reinforcement Learning setup where an agent interacts with an environment in observation-reward-action cycles without any (esp.\ MDP) assumptions on the environment. State aggregation and more generally feature reinforcement learning is concerned with mapping histories/raw-states to reduced/aggregated states. The idea behind both is that the resulting reduced process (approximately) forms a small stationary finite-state MDP, which can then be efficiently solved or learnt. We considerably generalize existing aggregation results by showing that even if the reduced process is not an MDP, the (q-)value functions and (optimal) policies of an associated MDP with same state-space size solve the original problem, as long as the solution can approximately be represented as a function of the reduced states. This implies an upper bound on the required state space size that holds uniformly for all RL problems. It may also explain why RL algorithms designed for MDPs sometimes perform well beyond MDPs.Comment: 28 LaTeX pages. 8 Theorem

Iterative Budgeted Exponential Search

We tackle two long-standing problems related to re-expansions in heuristic search algorithms. For graph search, A* can require Ω(2^n) expansions, where n is the number of states within the final f bound. Existing algorithms that address this problem like B and B' improve this bound to Ω(n^2). For tree search, IDA* can also require Ω(n^2) expansions. We describe a new algorithmic framework that iteratively controls an expansion budget and solution cost limit, giving rise to new graph and tree search algorithms for which the number of expansions is O(n log C*), where C* is the optimal solution cost. Our experiments show that the new algorithms are robust in scenarios where existing algorithms fail. In the case of tree search, our new algorithms have no overhead over IDA* in scenarios to which IDA* is well suited and can therefore be recommended as a general replacement for IDA*

‘I can’t accept that feeling’: Relationships between interoceptive awareness, mindfulness and eating disorder symptoms in females with, and at-risk of an eating disorder

Mindfulness based therapies (MBTs) for eating disorders show potential benefit for outcomes yet evidence is scarce regarding the mechanisms by which they influence remission from symptoms. One way that mindfulness approaches create positive outcomes is through enhancement of emotion regulation skills. Maladaptive emotion regulation is a key psychological feature of all eating disorders. The aim of the current study was to identify facets of emotion regulation involved in the relationship between mindfulness and maladaptive eating behaviours. In three cross-sectional studies, clinical (n=39) and non-clinical (n=137 and 119) female participants completed: 1) the Eating Disorder Inventory (EDI) eating specific scales (drive-for-thinness and bulimia) and the EDI psychological symptom scales (emotion dysregulation and interoceptive deficits); and 2) mindfulness, impulsivity, and emotion regulation questionnaires. In all samples mindfulness was significantly and inversely associated with EDI eating and psychological symptom scales, and impulsivity. In non-clinical samples interoceptive deficits mediated the relationship between mindfulness and EDI eating specific scales. Non-acceptance of emotional experience, a facet of interoceptive awareness, mediated the relationship between mindfulness and eating specific EDI scores. Further investigations could verify relationships identified so that mindfulness based approaches can be optimised to enhance emotion regulation skills in sufferers, and those at-risk, of eating disorders

The diverse nature of island isolation and its effect on land bridge insular faunas

Aim: Isolation is a key factor in island biology. It is usually defined as the distance to the geographically nearest mainland, but many other definitions exist. We explored how testing different isolation indices affects the inference of impacts of isolation on faunal characteristics. We focused on land bridge islands and compared the relationships of many spatial and temporal (i.e., through time) isolation indices with community‐, population‐ and individual‐level characteristics (species richness, population density and body size, respectively). Location: Aegean Sea islands, Greece. Time period: Current. Taxon: Many animal taxa. Methods: We estimated 21 isolation indices for 205 islands and recorded species richness data for 15 taxa (invertebrates and vertebrates). We obtained body size data for seven lizard species and population density data for three. We explored how well indices predict each characteristic, in each taxon, by conducting a series of ordinary least squares regressions (controlling for island area when needed) and a meta‐analysis. Results: Isolation was significantly (and negatively) associated with species richness in 10 of 15 taxa. It was significantly (and positively) associated with body size in only one of seven species and was not associated with population density. The effect of isolation on species richness was much weaker than that of island area, regardless of the index tested. Spatial indices generally out‐performed temporal indices, and indices directly related to the mainland out‐performed those related mainly to neighbouring islands. No index was universally superior to others, including the distance to the geographically nearest mainland. Main conclusions: The choice of index can alter our perception of the impacts of isolation on biological patterns. The nearly automatic, ubiquitous use of distance to the geographically nearest mainland misrepresents the complexity of the effects of isolation. We recommend the simultaneous testing of several indices that represent different aspects of isolation, in order to produce more constructive and thorough investigations and avoid imprecise inference

‘I can’t accept that feeling’: Relationships between interoceptive awareness, mindfulness and eating disorder symptoms in females with, and at-risk of an eating disorder

Mindfulness based therapies (MBTs) for eating disorders show potential benefit for outcomes yet evidence is scarce regarding the mechanisms by which they influence remission from symptoms. One way that mindfulness approaches create positive outcomes is through enhancement of emotion regulation skills. Maladaptive emotion regulation is a key psychological feature of all eating disorders. The aim of the current study was to identify facets of emotion regulation involved in the relationship between mindfulness and maladaptive eating behaviours. In three cross-sectional studies, clinical (n=39) and non-clinical (n=137 and 119) female participants completed: 1) the Eating Disorder Inventory (EDI) eating specific scales (drive-for-thinness and bulimia) and the EDI psychological symptom scales (emotion dysregulation and interoceptive deficits); and 2) mindfulness, impulsivity, and emotion regulation questionnaires. In all samples mindfulness was significantly and inversely associated with EDI eating and psychological symptom scales, and impulsivity. In non-clinical samples interoceptive deficits mediated the relationship between mindfulness and EDI eating specific scales. Non-acceptance of emotional experience, a facet of interoceptive awareness, mediated the relationship between mindfulness and eating specific EDI scores. Further investigations could verify relationships identified so that mindfulness based approaches can be optimised to enhance emotion regulation skills in sufferers, and those at-risk, of eating disorders