Why bayesian “evidence for H1” in one condition and bayesian “evidence for H0” in another condition does not mean good-enough bayesian evidence for a difference between the conditions

Psychologists are often interested in whether an independent variable has a different effect in condition A than in condition B. To test such a question, one needs to directly compare the effect of that variable in the two conditions (i.e., test the interaction). Yet many researchers tend to stop when they find a significant test in one condition and a nonsignificant test in the other condition, deeming this as sufficient evidence for a difference between the two conditions. In this Tutorial, we aim to raise awareness of this inferential mistake when Bayes factors are used with conventional cutoffs to draw conclusions. For instance, some researchers might falsely conclude that there must be good-enough evidence for the interaction if they find good-enough Bayesian evidence for the alternative hypothesis, H1, in condition A and good-enough Bayesian evidence for the null hypothesis, H0, in condition B. The case study we introduce highlights that ignoring the test of the interaction can lead to unjustified conclusions and demonstrates that the principle that any assertion about the existence of an interaction necessitates the direct comparison of the conditions is as true for Bayesian as it is for frequentist statistics. We provide an R script of the analyses of the case study and a Shiny app that can be used with a 2 × 2 design to develop intuitions on this issue, and we introduce a rule of thumb with which one can estimate the sample size one might need to have a well-powered design

A Combinatorial Polynomial Algorithm for the Linear Arrow-Debreu Market

We present the first combinatorial polynomial time algorithm for computing the equilibrium of the Arrow-Debreu market model with linear utilities.Comment: Preliminary version in ICALP 201

Statistical Curse of the Second Half Rank

In competitions involving many participants running many races the final rank is determined by the score of each participant, obtained by adding its ranks in each individual race. The "Statistical Curse of the Second Half Rank" is the observation that if the score of a participant is even modestly worse than the middle score, then its final rank will be much worse (that is, much further away from the middle rank) than might have been expected. We give an explanation of this effect for the case of a large number of races using the Central Limit Theorem. We present exact quantitative results in this limit and demonstrate that the score probability distribution will be gaussian with scores packing near the center. We also derive the final rank probability distribution for the case of two races and we present some exact formulae verified by numerical simulations for the case of three races. The variant in which the worst result of each boat is dropped from its final score is also analyzed and solved for the case of two races.Comment: 16 pages, 10 figure

Axiomatic Characterization of the Mean Function on Trees

A mean of a sequence π = (x1, x2, . . . , xk) of elements of a finite metric space (X, d) is an element x for which is minimum. The function Mean whose domain is the set of all finite sequences on X and is defined by Mean(π) = { x | x is a mean of π } is called the mean function on X. In this paper the mean function on finite trees is characterized axiomatically

The efficacy and experience of MoodGroup, an online group cognitive behavioural-based intervention for the treatment of depression in Australian adults.

Depression is a serious mental health condition affecting approximately 4.1% of Australian adults. Group cognitive behavioural therapy (gCBT) delivered online can potentially provide affordable, accessible, and efficacious treatment to individuals with depression. Online gCBT is an emerging treatment modality and there is a need to broaden the literature regarding its utility and efficacy. Controlled trials are needed to determine the efficacy of online gCBT for depression. This thesis aimed to examine the efficacy, onset of response, group processes, usability, and key stakeholder perspectives of MoodGroup, an online synchronous gCBT intervention for the treatment of clinical depression in Australian adults. In total 92 Australian adults (73 female and 19 male) with a depressive disorder were assigned to either a treatment or wait-list control (WLC) group. Provisional psychologists delivered the treatment intervention with groups of up to eight participants at a time. MoodGroups were hosted in synchronous virtual therapeutic rooms and ran for two hours a week for nine weeks. Participants completed weekly readings and homework activities. They also completed fortnightly outcome measures assessing their depressive symptoms, psychological distress, quality of life (QoL) and group climate perceptions. The usability of the intervention was assessed using weekly online session evaluation questionnaires. A mixed-method approach including linear mixed modelling and thematic content analysis assessed the efficacy and usability of MoodGroup. Additionally, focus groups and interviews were conducted with the MoodGroup facilitators and clinical supervisor to obtain their perspectives on the strengths and limitations of the intervention and recommendations for improvement. The findings of the efficacy trial demonstrated effect sizes favouring the treatment over the control group for all variables, with strong effects (d = 0.65-0.74) noted for measures of depression, psychological distress, anxiety and dysfunctional thoughts. Compared to the WLC, QoL was significantly improved in MoodGroup recipients. Treatment effects were largely maintained over time. The group processes displayed in the MoodGroup intervention were similar to those observed in successful face-to-face gCBT interventions. Furthermore, group climate variables predicted outcome at post-treatment and six-month follow-up. Similar to face-to-face groups, the majority of symptom improvement occurred in the early stages of the MoodGroup intervention. Additionally, early improvement and response to treatment was predictive of treatment gains at the conclusion of the MoodGroup intervention and at six-month follow-up. The usability analysis demonstrated the high usability and acceptability of the intervention. Additionally, valuable insights obtained from the MoodGroup facilitators and clinical supervisor will guide changes to future versions of the intervention and recommendations for group online interventions in general. The major limitations of this research included the small sample size, high rate of attrition and reliance on self-reported data. In conclusion, MoodGroup demonstrated good usability and efficacy. Findings from this thesis contribute to the emerging literature surrounding online group therapy interventions and guide recommendations for future group-based online interventions

Utilitarian Collective Choice and Voting

In his seminal Social Choice and Individual Values, Kenneth Arrow stated that his theory applies to voting. Many voting theorists have been convinced that, on account of Arrow’s theorem, all voting methods must be seriously flawed. Arrow’s theory is strictly ordinal, the cardinal aggregation of preferences being explicitly rejected. In this paper I point out that all voting methods are cardinal and therefore outside the reach of Arrow’s result. Parallel to Arrow’s ordinal approach, there evolved a consistent cardinal theory of collective choice. This theory, most prominently associated with the work of Harsanyi, continued the older utilitarian tradition in a more formal style. The purpose of this paper is to show that various derivations of utilitarian SWFs can also be used to derive utilitarian voting (UV). By this I mean a voting rule that allows the voter to score each alternative in accordance with a given scale. UV-k indicates a scale with k distinct values. The general theory leaves k to be determined on pragmatic grounds. A (1,0) scale gives approval voting. I prefer the scale (1,0,-1) and refer to the resulting voting rule as evaluative voting. A conclusion of the paper is that the defects of conventional voting methods result not from Arrow’s theorem, but rather from restrictions imposed on voters’ expression of their preferences. The analysis is extended to strategic voting, utilizing a novel set of assumptions regarding voter behavior

Why physicians are lousy gatekeepers: Sicklisting decisions when patients have private information on symptoms

In social insurance systems that grant workers paid sick leave, physicians act as gatekeepers, supposedly granting sickness certificates to the sick and not to shirkers. Previous research has emphasized the physician's superior ability to judge patients' need of treatment and potential collusion with the patient vis‐á‐vis an insurer. What is less well understood is the role of patients' private information. We explore the case where patients have private information about the presence of nonverifiable symptoms. Anyone can then claim to experience such symptoms, reducing physicians' ability to distinguish between sick patients and shirkers. Doubting a patients' reported symptoms may prevent good medical treatment of the truly sick. We show that for all parameter values, the Bayesian Nash equilibrium is that some physicians trust all claims of nonverifiable symptoms, sicklisting shirkers as well as sick; for many values, every physician is trusting. In particular, if physician strategies are observable by patients, extremely strong gatekeeping preferences are required to make physicians mistrust. To limit unwarranted sicklisting, policies reducing the benefits of shirking for healthy workers may be better suited than attempts to convince physicians to be strict.publishedVersio

Modularity and Optimality in Social Choice

Marengo and the second author have developed in the last years a geometric model of social choice when this takes place among bundles of interdependent elements, showing that by bundling and unbundling the same set of constituent elements an authority has the power of determining the social outcome. In this paper we will tie the model above to tournament theory, solving some of the mathematical problems arising in their work and opening new questions which are interesting not only from a mathematical and a social choice point of view, but also from an economic and a genetic one. In particular, we will introduce the notion of u-local optima and we will study it from both a theoretical and a numerical/probabilistic point of view; we will also describe an algorithm that computes the universal basin of attraction of a social outcome in O(M^3 logM) time (where M is the number of social outcomes).Comment: 42 pages, 4 figures, 8 tables, 1 algorithm

Adaptive Investment Strategies For Periodic Environments

In this paper, we present an adaptive investment strategy for environments with periodic returns on investment. In our approach, we consider an investment model where the agent decides at every time step the proportion of wealth to invest in a risky asset, keeping the rest of the budget in a risk-free asset. Every investment is evaluated in the market via a stylized return on investment function (RoI), which is modeled by a stochastic process with unknown periodicities and levels of noise. For comparison reasons, we present two reference strategies which represent the case of agents with zero-knowledge and complete-knowledge of the dynamics of the returns. We consider also an investment strategy based on technical analysis to forecast the next return by fitting a trend line to previous received returns. To account for the performance of the different strategies, we perform some computer experiments to calculate the average budget that can be obtained with them over a certain number of time steps. To assure for fair comparisons, we first tune the parameters of each strategy. Afterwards, we compare the performance of these strategies for RoIs with different periodicities and levels of noise.Comment: Paper submitted to Advances in Complex Systems (November, 2007) 22 pages, 9 figure