Sequential Dynamic Leadership Inference Using Bayesian Monte Carlo Methods

Hierarchy and leadership interactions commonly occur in animal groups, crowds of people and in vehicle motions. Such interactions are often affected by one or more individuals who possess key domain information (e.g. final destination, environmental constraints and best routes) or pertinent traits (e.g. better navigation, sensing and decision making capabilities) compared with the rest of the group. This paper presents a framework for the automatic identification of group structure and leadership from noisy sensory observations of tracked groups. Accordingly, a new leader-follower model is developed which assumes the dynamics of the group to be a multivariate Ornstein–Uhlenbeck process with the designated leader(s) drifting to the destination and followers reverting to the leaders’ state. Sequential Monte Carlo (SMC) approaches, and specifically the sequential Markov chain Monte Carlo (SMCMC) approach, are adopted to infer, probabilistically, the evolving leadership structure. A Rao-Blackwellisation scheme is employed such that the kinematic state of the objects in the group is inferred in closed form by Kalman filtering. Experiments show that the proposed techniques can successfully determine the leadership structures in challenging scenarios with a corresponding enhancement in tracking accuracy through direct consideration of the leadership interactions of the group

Role model effects on women's political engagement: Observational and experimental approaches to measurement & two studies on mediation

Contributing to a growing debate about `symbolic' or non-policy effects of gender-balanced legislatures, my thesis sets out to tackle issues of (a) measurement, combining experimental and observational evidence of the effect of female politicians as role models on women's political engagement; and (b) mediation, considering the underlying mechanisms convincing on the individual-level of voter psychology, explaining why role models are powerful in engaging fellow women in the electorate. Firstly, I triangulate results from an eye-tracking experiment investigating attentional bias to gender balance in manipulated picture stimuli of political groups; an online experiment investigating measures of psychological engagement with politics as a function of gender balance in the same picture stimuli; and British Election Study panel data investigating campaign effects on psychological engagement with politics as a function of the gender balance among candidates running in the 2010 and 2015 UK parliamentary constituencies. My results suggest two general types of role model effects: one of `tokenism' where women's striking minority presence impacts political attention and the probability of learning about politics, and one of `linear' effects where a gradual increase in women's presence in political groups towards parity translates into a gradual increase in political self-efficacy and confidence about political knowledge. Secondly, I develop and test hypotheses about mediation in terms of implicit mechanisms not requiring that citizens consider the policy output of their representatives, drawing heavily on the stereotype threat literature especially on the role of affect. Using a more classical, regression-based approach to mediation analysis, along with a novel crossover experiment or `design-based' mediation analysis, I present preliminary evidence that, following exposure to role models, women experienced fewer self-evaluative threats as evidenced by anxiety, explaining effects on self-efficacy in politics. I present an additional study scrutinising affect, and show that the action-oriented anger may result in approach of the source of threat, reversing stereotype threat effects under `men-only' politics. Thirdly, I develop and test hypotheses about mediation in terms of instrumental mechanisms that do require expectations or associations about policy output. Through similar approaches to mediation analysis, I show that though women expect better policy across two domains with more female politicians on board, greater competency attributed to elites is, if anything, negatively related to self-efficacy in politics. In a full-experimental study, I find no evidence that women's greater self-efficacy is due to expectations about women-friendly policy pursued by role models

From Groups to Leaders and Back. Exploring Mutual Predictability Between Social Groups and Their Leaders

Recently, social theories and empirical observations identified small groups and leaders as the basic elements which shape a crowd. This leads to an intermediate level of abstraction that is placed between the crowd as a flow of people, and the crowd as a collection of individuals. Consequently, automatic analysis of crowds in computer vision is also experiencing a shift in focus from individuals to groups and from small groups to their leaders. In this chapter, we present state-of-the-art solutions to the groups and leaders detection problem, which are able to account for physical factors as well as for sociological evidence observed over short time windows. The presented algorithms are framed as structured learning problems over the set of individual trajectories. However, the way trajectories are exploited to predict the structure of the crowd is not fixed but rather learned from recorded and annotated data, enabling the method to adapt these concepts to different scenarios, densities, cultures, and other unobservable complexities. Additionally, we investigate the relation between leaders and their groups and propose the first attempt to exploit leadership as prior knowledge for group detection

A decision-theoretic approach for segmental classification

This paper is concerned with statistical methods for the segmental classification of linear sequence data where the task is to segment and classify the data according to an underlying hidden discrete state sequence. Such analysis is commonplace in the empirical sciences including genomics, finance and speech processing. In particular, we are interested in answering the following question: given data $y$ and a statistical model $\pi(x,y)$ of the hidden states $x$, what should we report as the prediction $\hat{x}$ under the posterior distribution $\pi (x|y)$? That is, how should you make a prediction of the underlying states? We demonstrate that traditional approaches such as reporting the most probable state sequence or most probable set of marginal predictions can give undesirable classification artefacts and offer limited control over the properties of the prediction. We propose a decision theoretic approach using a novel class of Markov loss functions and report $\hat{x}$ via the principle of minimum expected loss (maximum expected utility). We demonstrate that the sequence of minimum expected loss under the Markov loss function can be enumerated exactly using dynamic programming methods and that it offers flexibility and performance improvements over existing techniques. The result is generic and applicable to any probabilistic model on a sequence, such as Hidden Markov models, change point or product partition models.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS657 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org