Social sensing for psychology: Automated interpersonal behavior assessment

In this article, we show how the use of state-of-the-art methods in computer science based on machine perception and learning allows the unobtrusive capture and automated analysis of interpersonal behavior in real time (social sensing). Given the high ecological validity of the behavioral sensing, the ease of behavioral-cue extraction for large groups over long observation periods in the field, the possibility of investigating completely new research questions, and the ability to provide people with immediate feedback on behavior, social sensing will fundamentally impact psychology.

Three essays on the psychology of investment and financial markets

Préface My thesis consists of three essays where I consider equilibrium asset prices and investment strategies when the market is likely to experience crashes and possibly sharp windfalls. Although each part is written as an independent and self contained article, the papers share a common behavioral approach in representing investors preferences regarding to extremal returns. Investors utility is defined over their relative performance rather than over their final wealth position, a method first proposed by Markowitz (1952b) and by Kahneman and Tversky (1979), that I extend to incorporate preferences over extremal outcomes. With the failure of the traditional expected utility models in reproducing the observed stylized features of financial markets, the Prospect theory of Kahneman and Tversky (1979) offered the first significant alternative to the expected utility paradigm by considering that people focus on gains and losses rather than on final positions. Under this setting, Barberis, Huang, and Santos (2000) and McQueen and Vorkink (2004) were able to build a representative agent optimization model which solution reproduced some of the observed risk premium and excess volatility. The research in behavioral finance is relatively new and its potential still to explore. The three essays composing my thesis propose to use and extend this setting to study investors behavior and investment strategies in a market where crashes and sharp windfalls are likely to occur. In the first paper, the preferences of a representative agent, relative to time varying positive and negative extremal thresholds are modelled and estimated. A new utility function that conciliates between expected utility maximization and tail-related performance measures is proposed. The model estimation shows that the representative agent preferences reveals a significant level of crash aversion and lottery-pursuit. Assuming a single risky asset economy the proposed specification is able to reproduce some of the distributional features exhibited by financial return series. The second part proposes and illustrates a preference-based asset allocation model taking into account investors crash aversion. Using the skewed t distribution, optimal allocations are characterized as a resulting tradeoff between the distribution four moments. The specification highlights the preference for odd moments and the aversion for even moments. Qualitatively, optimal portfolios are analyzed in terms of firm characteristics and in a setting that reflects real-time asset allocation, a systematic over-performance is obtained compared to the aggregate stock market. Finally, in my third article, dynamic option-based investment strategies are derived and illustrated for investors presenting downside loss aversion. The problem is solved in closed form when the stock market exhibits stochastic volatility and jumps. The specification of downside loss averse utility functions allows corresponding terminal wealth profiles to be expressed as options on the stochastic discount factor contingent on the loss aversion level. Therefore dynamic strategies reduce to the replicating portfolio using exchange traded and well selected options, and the risky stock.

Counterfactuals and causal inference: Methods and principles for social research

"Most quantitative empirical analyses are motivated by the desire to estimate the causal effect of an independent variable on a dependent variable. Although the randomized experiment is the most powerful design for this task, in most social science research done outside of psychology, experimental designs are infeasible. (Winship & Morgan, 1999, p. 659)." This quote from earlier work by Winship and Morgan, which was instrumental in setting the groundwork for their book, captures the essence of our review of Morgan and Winship's book: It is about causality in nonexperimental settings.

Psychometric properties of the Marlowe-Crowne Social Desirability Scale in eight African countries and Switzerland

The purpose of this study was to assess the cross-cultural validity of the Marlowe-Crowne Social Desirability scale short form C, in a large sample of French-speaking participants from eight African countries and Switzerland. Exploratory and confirmatory analyses suggested retaining a two-factor structure. Item bias detection according to country was conducted for all 13 items and effect was calculated with R2. For the two-factor solution, 9 items were associated with a negligible effect size, 3 items with a moderate one, and 1 item with a large one. A series of analyses of covariance considering the acquiescence variable as a covariate showed that the acquiescence tendency does not contribute to the bias at item level. This research indicates that the psychometric properties of this instrument do not reach a scalar equivalence but that a culturally reliable measurement of social desirability could be developed.

Studying social interactions through immersive virtual environment technology: Virtues, pitfalls, and future challenges

Thegoalofthepresentreviewistoexplainhowimmersivevirtualenvironmenttechnology(IVET)canbeusedforthestudyofsocialinteractionsandhowtheuseofvirtualhumansinimmersivevirtualenvironmentscanadvanceresearchandapplicationinmanydifferentfields.Researchersstudyingindividualdifferencesinsocialinteractionsaretypicallyinterestedinkeepingthebehaviorandtheappearanceoftheinteractionpartnerconstantacrossparticipants.WithIVETresearchershavefullcontrolovertheinteractionpartners,canstandardizethemwhilestillkeepingthesimulationrealistic.Virtualsimulationsarevalid:growingevidenceshowsthatindeedstudiesconductedwithIVETcanreplicatesomewell-knownfindingsofsocialpsychology.Moreover,IVETallowsresearcherstosubtlymanipulatecharacteristicsoftheenvironment(e.g.,visualcuestoprimeparticipants)orofthesocialpartner(e.g.,his/herrace)toinvestigatetheirinfluencesonparticipants'behaviorandcognition.Furthermore,manipulationsthatwouldbedifficultorimpossibleinreallife(e.g.,changingparticipants'height)canbeeasilyobtainedwithIVET.Besidetheadvantagesfortheoreticalresearch,weexplorethemostrecenttrainingandclinicalapplicationsofIVET,itsintegrationwithothertechnologies(e.g.,socialsensing)andfuturechallengesforresearchers(e.g.,makingthecommunicationbetweenvirtualhumansandparticipantssmoother).