I’m Jack VanDrunen. I am a doctoral candidate in the department of Logic and Philosophy of Science at the University of California, Irvine. In my dissertation project, I develop and defend a version of radical probabilism.
Before beginning the Ph.D., I earned a bachelor’s (B.S.) in Computer Science, also at UC Irvine. I also did software engineering work and machine learning research at a startup that was building cashierless stores.
My published research thus far has been in evolutionary game theory and human problem solving. In addition to the work listed below, I am engaged in ongoing research projects with my colleagues Saira Khan and Eyob Zewdie.
Traveling Salesperson Problem with Simple Obstacles (Computational Brain and Behavior)
The objective of the traveling salesperson problem (TSP) is to find a shortest tour through all nodes in a graph. Euclidean TSPs are traditionally represented with "cities" placed on a 2D plane. When straight line obstacles are added to the plane, a tour has to visit all cities while going around obstacles. The resulting problem with obstacles remains metric, but is not Euclidean because the shortest paths are no longer straight lines. We first revise a previous version of multiresolution graph pyramid by modifying the hierarchical clustering stage. Next, we describe two new experiments with human subjects. In the first experiment, the effect of the length of obstacles on the quality of tours produced by subjects was tested with three problem sizes. Long obstacles affect the tours to a greater degree than short obstacles, but long obstacles create obvious clusters and limit the ways in which the tours can be produced. In the second experiment we evaluated the degree to which Multidimensional Scaling (MDS) can compensate for the presence of obstacles. The results show that although MDS approximation can compensate to a large degree for the presence of obstacles, it cannot fully account for human performance. This fact suggests that mental representation of a TSP with obstacles is not Euclidean. Instead, it is likely to be based on hierarchical clustering in which pairwise distances represent the shortest paths around obstacles.
VanDrunen, J., Nam, K., Beers, M., and Pizlo, Z. "Traveling salesperson problem with simple obstacles: The role of multidimensional scaling and the role of clustering." Computational Brain and Behavior (2022). https://doi.org/10.1007/s42113-022-00155-0
[Open Access on SpringerLink]
Naturalizing Natural Salience (British Journal for the Philosophy of Science)
Grice, Lewis, and Skyrms propose similar distinctions between kinds of meaning. The meaning of terms in human language, as Lewis and Skyrms had it, is ‘conventional’. Skyrms presents models showing how it is possible for conventional meaning to evolve in a population without reliance on pre-existing meaning. But one might think of conventionality as coming in degrees, based on whether the evolutionary process begins with ‘natural saliences’. We propose a theory of natural salience and several extensions of Skyrms’s models to capture this notion. These models reveal that natural saliences can hinder, as well as help, the evolution of language.
VanDrunen, J. and Herrmann, D. A. "Naturalizing natural salience." British Journal for the Philosophy of Science. https://doi.org/10.1086/725654
[Preprint on PhilSci-Archive]
Sifting the Signal from the Noise (British Journal for the Philosophy of Science)
Signalling games are useful for understanding how language emerges. In the standard models the dynamics in some sense already knows what the signals are, even if they do not yet have meaning. In this paper we relax this assumption, and develop a simple model we call an `attention game' in which agents have to learn which feature in their environment is the signal. We demonstrate that simple reinforcement learning agents can still learn to coordinate in contexts in which (i) the agents do not already know what the signal is and (ii) the other features in the agents' environment are uncorrelated with the signal. Furthermore, we show that, in cases in which other features are correlated with the signal, there is a surprising trade-off between learning what the signal is, and success in action. We show that the mutual information between a signal and a feature plays a key role in governing the accuracy and attention of the agent.
Herrmann, D. A. and VanDrunen, J. "Sifting the signal from the noise." British Journal for the Philosophy of Science. https://doi.org/10.1086/720805
[Preprint on PhilSci-Archive]
Language Games and the Emergence of Discourse (Synthese)
Wittgenstein (1958) used the notion of a language game to illustrate how language is interwoven with action. Here we consider how successful linguistic discourse of the sort he described might emerge in the context of a self-assembling evolutionary game. More specifically, we consider how discourse and coordinated action might self-assemble in the context of two generalized signaling games. The first game shows how prospective language users might learn to initiate meaningful discourse. The second shows how more subtle varieties of discourse might co-emerge with a meaningful language.
Barrett, J. A. and VanDrunen, J. "Language games and the emergence of discourse." Synthese 200, 159 (2022). https://doi.org/10.1007/s11229-022-03645-7
[Open Access on SpringerLink]
System that Performs Selective Manual Review of Shopping Carts in an Automated Store
An automated store that calculates a confidence score for virtual shopping carts of shoppers, and selects carts for manual review based on these scores. Carts with low confidence scores may be more likely to contain errors, so prioritizing manual review of these carts is a cost-effective method of improving overall accuracy. A cart confidence score may be a function of factors such as confidence in the trajectory of the shopper generated by the store tracking system, confidence in the events (such as taking an item from a shelf) that affect the cart, and confidence that events are attributed to the correct shopper. Situations that make tracking, item identification, or attribution more complex may reduce confidence levels. For example, attribution confidence may be low when multiple shoppers are near an event, and item confidence may be low if the probabilistic classifier that identifies the item assigns nontrivial probabilities to multiple items.
Buibas, M., Quinn, J., Bapst, A., Khorsandi, R., Shah, N., VanDrunen, J., Wildie, M., and Yousefisahi, S. "System that Performs Selective Manual Review of Shopping Carts in an Automated Store." US20210158430A1[Google Patents]