A Bayesian Hierarchical Model for Evaluating Forensic Footwear Evidence
Neil A. Spencer and Jared Murray ; Annals of Applied Statistics, 14(3) 1449-1470
When a latent shoeprint is discovered at a crime scene, forensic analysts inspect it for distinctive patterns of wear such as scratches and holes (known as accidentals) on the source shoe's sole. If its accidentals correspond to those of a suspect's shoe, the print can be used as forensic evidence to place the suspect at the crime scene. The strength of this evidence depends on the random match probability---the chance that a shoe chosen at random would match the crime scene print's accidentals. Evaluating random match probabilities requires an accurate model for the spatial distribution of accidentals on shoe soles. A recent report by the President's Council of Advisors in Science and Technology criticized existing models in the literature, calling for new empirically validated techniques. We respond to this request with a new spatial point process model for accidental locations, developed within a hierarchical Bayesian framework. We treat the tread pattern of each shoe as a covariate, allowing us to pool information across large heterogeneous databases of shoes. Existing models ignore this information; our results show that including it leads to significantly better model fit. We demonstrate this by fitting our model to one such database.
A Model of Fake Data in Data-driven Analysis
A Model of Fake Data in Data-driven Analysis. Journal of Machine Learning Research 21, 1-26.
Data-driven analysis has been increasingly used in various decision making processes. With more sources, including reviews, news, and photos, that can now be used for data analysis, the authenticity of data sources is in doubt. While previous literature attempted to detect fake data piece by piece, in the current work, we try to capture the fake data sender's strategic behavior to detect the fake data source. Specifically, we model the tension between a data receiver who makes data-driven decisions and a fake data sender who benefits from misleading the receiver. We propose a potentially infinite horizon continuous time game-theoretic model with asymmetric information to capture the fact that the receiver does not initially know the existence of fake data and learns about it during the course of the game. We use point processes to model the data traffic, where each piece of data can occur at any discrete moment in a continuous time flow. We fully solve the model and employ numerical examples to illustrate the players' strategies and payoffs for insights. Specifically, our results show that maintaining some suspicion about the data sources can be very helpful to the data receiver.
A New Class of Time Dependent Latent Factor Models with Applications
A New Class of Time Dependent Latent Factor Models with Applications. Journal of Machine Learning Research 21(26-47), 1-24.
In many applications, observed data are influenced by some combination of latent causes. For example, suppose sensors are placed inside a building to record responses such as temperature, humidity, power consumption and noise levels. These random, observed responses are typically affected by many unobserved, latent factors (or features) within the building such as the number of individuals, the turning on and off of electrical devices, power surges, etc. These latent factors are usually present for a contiguous period of time before disappearing; further, multiple factors could be present at a time. This paper develops new probabilistic methodology and inference methods for random object generation influenced by latent features exhibiting temporal persistence. Every datum is associated with subsets of a potentially infinite number of hidden, persistent features that account for temporal dynamics in an observation. The ensuing class of dynamic models constructed by adapting the Indian Buffet Process --- a probability measure on the space of random, unbounded binary matrices --- finds use in a variety of applications arising in operations, signal processing, biomedicine, marketing, image analysis, etc. Illustrations using synthetic and real data are provided.
Advanced Technology and Endtime in Organizations: A Doomsday for Collaborative Creativity?
Sirkka L. Jarvenpaa and Liisa Välikangas; Academy of Management Perspectives, 34(4) 566-584
Our capacity to tackle grand challenges facing humanity depends on collaborative creativity. Increasingly, such collaborative creativity is affected by advanced technology such as mobile technology, virtual communications, and algorithmic computing. We use a temporal lens to study the potential of advanced technology to influence collaborative creativity. Prior studies have found that inner time and social time are critical for collaborative creativity. To creatively and purposefully contribute to collaboration, inner time—a temporal capacity to reflect on actions, meaning, and consequences over time—is required. Also necessary is social time—the time spent with others—to practice giving and taking of multivocal ideas and perspectives. What has not been well scrutinized in the organization and management literature is whether advanced technology might suppress both inner time and social time. In this paper, we advance future-oriented conjectures on the potential role of advanced technology on such temporal capacity. Included in our projections is a futuristic doomsday in which advanced technology has extinguished inner time and social time and hence curtailed collaborative creativity. We advance policy considerations for avoiding such an “end-time” scenario in organizations and societies.
All Eyes on You: Public Consumption Contexts and Hedonic Adaptation to Products
Sunaina K. Chugani and Sunaina K. Chugani; Psychology and Marketing, 37(11) 1554-1570
Marketers have a keen interest in keeping customers happy past the point of product acquisition. However, consumer happiness with products typically declines over time, a process called “hedonic adaptation.” Understanding this process is essential for managing consumers' post‐acquisition experiences, and yet marketers have not explored how the ubiquitous social environment influences hedonic adaptation. We explore the effect of a social audience (i.e., the presence of others and the perception that those others are noticing you) on adaptation to positive products using two real‐world studies and one lab study. We show that a social audience can slow hedonic adaptation by cuing consumers to believe that others are admiring their product. This perceived admiration, in turn, helps consumers see the product through fresh, unadapted eyes. These findings help clarify the role of the consumption environment in adaptation, help explain why product happiness can vary by consumer over time, and show that the effects of social forces do not always occur at the moment of product acquisition.