Models of crowd behavior facilitate analysis and prediction of human group behavior, where people are affected by each other's presence. Unfortunately, existing models leave many open challenges. In particular, psychology models often offer only qualitative description, while computer science models are often simplistic, and are not reusable from one simulated phenomenon to the next. We propose a novel model of crowd behavior, based on Festinger's Social Comparison Theory (SCT). We propose a concrete algorithmic framework for SCT, and evaluate its implementation in several crowd behavior scenarios. Results from task measures and human judges evaluation shows that the SCT model produces improved results compared to base models from the literature.
Now here's a contagion that might not be so bad to encounter. A new analysis of the running habits of about 1.1 million people reveals that exercise is indeed contagious -- though its communicability depends on who's spreading it. The findings, published in the journal Nature Communications, also reveal that certain relationships are better at spreading the running bug than others -- and could have implications for the study of other social contagions, such as obesity and smoking. In recent years, researchers in a wide range of fields -- from economics and politics to medicine and computer science -- have begun to investigate the ways in which many of our individual decisions affect the decisions of our peers, and how behavioral changes may spread through a social network. "If behavioural contagions exist," the study authors wrote, "understanding how, when and to what extent they manifest in different behaviours will enable us to transition from independent intervention strategies to more effective interdependent interventions that incorporate individuals' social contexts into their treatments."
We consider sequential or active ranking of a set of n items based on noisy pairwise comparisons. Items are ranked according to the probability that a given item beats a randomly chosen item, and ranking refers to partitioning the items into sets of pre-specified sizes according to their scores. This notion of ranking includes as special cases the identification of the top-k items and the total ordering of the items. We first analyze a sequential ranking algorithm that counts the number of comparisons won, and uses these counts to decide whether to stop, or to compare another pair of items, chosen based on confidence intervals specified by the data collected up to that point. We prove that this algorithm succeeds in recovering the ranking using a number of comparisons that is optimal up to logarithmic factors. This guarantee does not require any structural properties of the underlying pairwise probability matrix, unlike a significant body of past work on pairwise ranking based on parametric models such as the Thurstone or Bradley-Terry-Luce models. It has been a long-standing open question as to whether or not imposing these parametric assumptions allows for improved ranking algorithms. For stochastic comparison models, in which the pairwise probabilities are bounded away from zero, our second contribution is to resolve this issue by proving a lower bound for parametric models. This shows, perhaps surprisingly, that these popular parametric modeling choices offer at most logarithmic gains for stochastic comparisons.
MPs on the Energy Committee have written to the new business secretary to demand no change to the current rules on price comparison websites. As part of its recent report on the energy market, the Competition and Markets Authority (CMA) said that such websites would no longer be required to show all the deals on offer. But the MPs said that would undermine consumer trust, and harm competition. The CMA rules will allow sites only to show deals on which they earn money. This reverses a decision by the regulator Ofcom.
Consumers need to hunt for deals as they do on the High Street when using switching websites for energy, holidays or insurance, a review has found. Price comparison websites worked best for car insurance and worst for broadband, the study by the Competition and Markets Authority (CMA) said. Consumers should use a variety of sites to get the best deals, it said. The CMA said it was investigating one website over contracts that risked raising home insurance prices. We welcome the opportunity to discuss this further with the CMA."