Troping the Enemy: Metaphor, Culture, and the Big Data Black Boxes of National Security

This article considers how cultural understanding is being brought into the work of the Intelligence Advanced Research Projects Activity (IARPA), through an analysis of its Metaphor program. It examines the type of social science underwriting this program, unpacks implications of the agency’s conception of metaphor for understanding so-called cultures of interest, and compares IARPA’s to competing accounts of how metaphor works to create cultural meaning. The article highlights some risks posed by key deficits in the Intelligence Community\u27s (IC) approach to culture, which relies on the cognitive linguistic theories of George Lakoff and colleagues. It also explores the problem of the opacity of these risks for analysts, even as such predictive cultural analytics are becoming a part of intelligence forecasting. This article examines the problem of information secrecy in two ways, by unpacking the opacity of “black box,” algorithm-based social science of culture for end users with little appreciation of their potential biases, and by evaluating the IC\u27s nontransparent approach to foreign cultures, as it underwrites national security assessments

Truth and Regret in Online Scheduling

We consider a scheduling problem where a cloud service provider has multiple units of a resource available over time. Selfish clients submit jobs, each with an arrival time, deadline, length, and value. The service provider's goal is to implement a truthful online mechanism for scheduling jobs so as to maximize the social welfare of the schedule. Recent work shows that under a stochastic assumption on job arrivals, there is a single-parameter family of mechanisms that achieves near-optimal social welfare. We show that given any such family of near-optimal online mechanisms, there exists an online mechanism that in the worst case performs nearly as well as the best of the given mechanisms. Our mechanism is truthful whenever the mechanisms in the given family are truthful and prompt, and achieves optimal (within constant factors) regret. We model the problem of competing against a family of online scheduling mechanisms as one of learning from expert advice. A primary challenge is that any scheduling decisions we make affect not only the payoff at the current step, but also the resource availability and payoffs in future steps. Furthermore, switching from one algorithm (a.k.a. expert) to another in an online fashion is challenging both because it requires synchronization with the state of the latter algorithm as well as because it affects the incentive structure of the algorithms. We further show how to adapt our algorithm to a non-clairvoyant setting where job lengths are unknown until jobs are run to completion. Once again, in this setting, we obtain truthfulness along with asymptotically optimal regret (within poly-logarithmic factors)

The Economics of Credence Goods: On the Role of Liability, Verifiability, Reputation and Competition

Credence goods markets are characterized by asymmetric information between sellers and consumers that may give rise to inefficiencies, such as under- and overtreatment or market break-down. We study in a large experiment with 936 participants the determinants for efficiency in credence goods markets. While theory predicts that either liability or verifiability yields efficiency, we find that liability has a crucial, but verifiability only a minor effect. Allowing sellers to build up reputation has little influence, as predicted. Seller competition drives down prices and yields maximal trade, but does not lead to higher efficiency as long as liability is violated.

A Model of Human Cooperation in Social Dilemmas

Social dilemmas are situations in which collective interests are at odds with private interests: pollution, depletion of natural resources, and intergroup conflicts, are at their core social dilemmas. Because of their multidisciplinarity and their importance, social dilemmas have been studied by economists, biologists, psychologists, sociologists, and political scientists. These studies typically explain tendency to cooperation by dividing people in proself and prosocial types, or appealing to forms of external control or, in iterated social dilemmas, to long-term strategies. But recent experiments have shown that cooperation is possible even in one-shot social dilemmas without forms of external control and the rate of cooperation typically depends on the payoffs. This makes impossible a predictive division between proself and prosocial people and proves that people have attitude to cooperation by nature. The key innovation of this article is in fact to postulate that humans have attitude to cooperation by nature and consequently they do not act a priori as single agents, as assumed by standard economic models, but they forecast how a social dilemma would evolve if they formed coalitions and then they act according to their most optimistic forecast. Formalizing this idea we propose the first predictive model of human cooperation able to organize a number of different experimental findings that are not explained by the standard model. We show also that the model makes satisfactorily accurate quantitative predictions of population average behavior in one-shot social dilemmas

On the discovery of social roles in large scale social systems

The social role of a participant in a social system is a label conceptualizing the circumstances under which she interacts within it. They may be used as a theoretical tool that explains why and how users participate in an online social system. Social role analysis also serves practical purposes, such as reducing the structure of complex systems to rela- tionships among roles rather than alters, and enabling a comparison of social systems that emerge in similar contexts. This article presents a data-driven approach for the discovery of social roles in large scale social systems. Motivated by an analysis of the present art, the method discovers roles by the conditional triad censuses of user ego-networks, which is a promising tool because they capture the degree to which basic social forces push upon a user to interact with others. Clusters of censuses, inferred from samples of large scale network carefully chosen to preserve local structural prop- erties, define the social roles. The promise of the method is demonstrated by discussing and discovering the roles that emerge in both Facebook and Wikipedia. The article con- cludes with a discussion of the challenges and future opportunities in the discovery of social roles in large social systems

The Economics of Credence Goods: On the Role of Liability, Verifiability, Reputation and Competition

Credence goods markets are characterized by asymmetric information between sellers and consumers that may give rise to inefficiencies, such as under- and overtreatment or market break-down. We study in a large experiment with 936 participants the determinants for efficiency in credence goods markets. While theory predicts that either liability or verifiability yields efficiency, we find that liability has a crucial, but verifiability only a minor effect. Allowing sellers to build up reputation has little influence, as predicted. Seller competition drives down prices and yields maximal trade, but does not lead to higher efficiency as long as liability is violated.competition, reputation, verifiability, liability, experiment, credence goods

Statistical fluctuations in pedestrian evacuation times and the effect of social contagion

Mathematical models of pedestrian evacuation and the associated simulation software have become essential tools for the assessment of the safety of public facilities and buildings. While a variety of models are now available, their calibration and test against empirical data are generally restricted to global, averaged quantities, the statistics compiled from the time series of individual escapes (" microscopic " statistics) measured in recent experiments are thus overlooked. In the same spirit, much research has primarily focused on the average global evacuation time, whereas the whole distribution of evacuation times over some set of realizations should matter. In the present paper we propose and discuss the validity of a simple relation between this distribution and the " microscopic " statistics, which is theoretically valid in the absence of correlations. To this purpose, we develop a minimal cellular automaton, with novel features that afford a semi-quantitative reproduction of the experimental " microscopic " statistics. We then introduce a process of social contagion of impatient behavior in the model and show that the simple relation under test may dramatically fail at high contagion strengths, the latter being responsible for the emergence of strong correlations in the system. We conclude with comments on the potential practical relevance for safety science of calculations based on " microscopic " statistics