Modeling the mobility of living organisms in heterogeneous landscapes: Does memory improve foraging success?

Thanks to recent technological advances, it is now possible to track with an unprecedented precision and for long periods of time the movement patterns of many living organisms in their habitat. The increasing amount of data available on single trajectories offers the possibility of understanding how animals move and of testing basic movement models. Random walks have long represented the main description for micro-organisms and have also been useful to understand the foraging behaviour of large animals. Nevertheless, most vertebrates, in particular humans and other primates, rely on sophisticated cognitive tools such as spatial maps, episodic memory and travel cost discounting. These properties call for other modeling approaches of mobility patterns. We propose a foraging framework where a learning mobile agent uses a combination of memory-based and random steps. We investigate how advantageous it is to use memory for exploiting resources in heterogeneous and changing environments. An adequate balance of determinism and random exploration is found to maximize the foraging efficiency and to generate trajectories with an intricate spatio-temporal order. Based on this approach, we propose some tools for analysing the non-random nature of mobility patterns in general.Comment: 14 pages, 4 figures, improved discussio

Estimating the Term Structure of Yield Spreads from Callable Corporate Bond Price Data

I extract credit pricing information from the prices of callable corporate debt, by disentangling the components of callable corporate bond prices associated with discounting at market interest rates, discounting for default risk, and optionality. The results include the first empirical analysis, in the setting of standard arbitrage-free term-structure models, of the time-series behavior of callable corporate bond yield spreads, explicitly incorporating the valuation of the American call options. As an application, I consider medium-quality callable issues of Occidental Petroleum Corporation, using a three-factor model for the term structures of benchmark LIBOR-dollar swap rates and for Occidental yield spreads.

Student-centric Model of Learning Management System Activity and Academic Performance: from Correlation to Causation

In recent years, there is a lot of interest in modeling students' digital traces in Learning Management System (LMS) to understand students' learning behavior patterns including aspects of meta-cognition and self-regulation, with the ultimate goal to turn those insights into actionable information to support students to improve their learning outcomes. In achieving this goal, however, there are two main issues that need to be addressed given the existing literature. Firstly, most of the current work is course-centered (i.e. models are built from data for a specific course) rather than student-centered; secondly, a vast majority of the models are correlational rather than causal. Those issues make it challenging to identify the most promising actionable factors for intervention at the student level where most of the campus-wide academic support is designed for. In this paper, we explored a student-centric analytical framework for LMS activity data that can provide not only correlational but causal insights mined from observational data. We demonstrated this approach using a dataset of 1651 computing major students at a public university in the US during one semester in the Fall of 2019. This dataset includes students' fine-grained LMS interaction logs and administrative data, e.g. demographics and academic performance. In addition, we expand the repository of LMS behavior indicators to include those that can characterize the time-of-the-day of login (e.g. chronotype). Our analysis showed that student login volume, compared with other login behavior indicators, is both strongly correlated and causally linked to student academic performance, especially among students with low academic performance. We envision that those insights will provide convincing evidence for college student support groups to launch student-centered and targeted interventions that are effective and scalable.Comment: 43 pages, 9 figures, 18 tables, Journal of Educational Data Mining (Initial Submission

Trusting the Explainers: Teacher Validation of Explainable Artificial Intelligence for Course Design

Deep learning models for learning analytics have become increasingly popular over the last few years; however, these approaches are still not widely adopted in real-world settings, likely due to a lack of trust and transparency. In this paper, we tackle this issue by implementing explainable AI methods for black-box neural networks. This work focuses on the context of online and blended learning and the use case of student success prediction models. We use a pairwise study design, enabling us to investigate controlled differences between pairs of courses. Our analyses cover five course pairs that differ in one educationally relevant aspect and two popular instance-based explainable AI methods (LIME and SHAP). We quantitatively compare the distances between the explanations across courses and methods. We then validate the explanations of LIME and SHAP with 26 semi-structured interviews of university-level educators regarding which features they believe contribute most to student success, which explanations they trust most, and how they could transform these insights into actionable course design decisions. Our results show that quantitatively, explainers significantly disagree with each other about what is important, and qualitatively, experts themselves do not agree on which explanations are most trustworthy. All code, extended results, and the interview protocol are provided at https://github.com/epfl-ml4ed/trusting-explainers.Comment: Accepted as a full paper at LAK 2023: The 13th International Learning Analytics and Knowledge Conference, March 13-17, 2023, Arlington, Texas, US

Economic Insights from Internet Auctions: A Survey

This paper surveys recent studies of Internet auctions. Four main areas of research are summarized. First, economists have documented strategic bidding in these markets and attempted to understand why sniping, or bidding at the last second, occurs. Second, some researchers have measured distortions from asymmetric information due, for instance, to the winner's curse. Third, we explore research about the role of reputation in online auctions. Finally, we discuss what Internet auctions have to teach us about auction design.

When and how to dismantle nuclear weapons

This paper first derives revenue-maximizing auctions with identity-specific externalities among all players (seller and buyers). Our main findings are as follows. Firstly, a modified second-price sealed-bid auction with appropriate entry fees and reserve price is revenue-maximizing. Secondly, seller may physically destroy the auctioned item if the item is unsold or use destroying the item as nonparticipation threat. Thirdly, the revenue-maximizing auction induces full participation of buyers. Fourthly, each losing buyer's payment includes an externality-correcting component that equals the allocative externality to him. These components eliminate the impact of externalities on strategic bidding behavior. The paper further studies revenue-maximizing auctions with financial externalities. One-to-one correspondences between revenue-maximizing auctions for settings with and without financial externalities are established through incorporating externality-correcting payments. This result provides a general method for designing revenue-maximizing auctions in different settings of financial externalities, since revenue-maximizing auctions can be obtained through transforming the revenue-maximizing auctions for the regular settings without externalities.Auctions design; Endogenous participation; Externality

Auditory Pattern Representations Under Conditions of Uncertainty—An ERP Study

The auditory system is able to recognize auditory objects and is thought to form predictive models of them even though the acoustic information arriving at our ears is often imperfect, intermixed, or distorted. We investigated implicit regularity extraction for acoustically intact versus disrupted six-tone sound patterns via event-related potentials (ERPs). In an exact-repetition condition, identical patterns were repeated; in two distorted-repetition conditions, one randomly chosen segment in each sound pattern was replaced either by white noise or by a wrong pitch. In a roving-standard paradigm, sound patterns were repeated 1–12 times (standards) in a row before a new pattern (deviant) occurred. The participants were not informed about the roving rule and had to detect rarely occurring loudness changes. Behavioral detectability of pattern changes was assessed in a subsequent behavioral task. Pattern changes (standard vs. deviant) elicited mismatch negativity (MMN) and P3a, and were behaviorally detected above the chance level in all conditions, suggesting that the auditory system extracts regularities despite distortions in the acoustic input. However, MMN and P3a amplitude were decreased by distortions. At the level of MMN, both types of distortions caused similar impairments, suggesting that auditory regularity extraction is largely determined by the stimulus statistics of matching information. At the level of P3a, wrong-pitch distortions caused larger decreases than white-noise distortions. Wrong-pitch distortions likely prevented the engagement of restoration mechanisms and the segregation of disrupted from true pattern segments, causing stronger informational interference with the relevant pattern informatio