Even superforecasters are guessing that they'll soon be obsolete. To live in time is to wonder what will happen next. In every human society, there are people who obsess over the world's patterns to predict the future. In antiquity, they told kings which stars would appear at nightfall. Today they build the quantitative models that nudge governments into opening spigots of capital.

To demonstrate the utility ofPREF-SHAP, we apply our method to a variety of synthetic and real-worlddatasets andshowthatricher andmoreinsightful explanations canbe obtainedoverthebaseline.

All strings generated by the CFG can be broken down into a (non-unique) tree of production ruleswiththenon-terminal startingsymbolS atitshead. Although each individual production rule is a simplereplacement operation, thecombination ofmanysuchrulescanspecific astringspacewith complex syntactical constraints. However,whensampling strings from the grammar, we found this simple sampling strategy to produce long and repetitive strings. In fact, these tasks are considerably more challenging than the common benchmarks used to test standard BO frameworks. We triedSEkernels withbothindividual andtiedlength scales across latentdimensions, however,this did not have a significant effect on performance, possibly due to difficulties in estimating many kernel parameters inthese low-data BO problems. This ranking matches the relative performance of the BO routines based on these surrogate models (Figure 7). Figure 7.d visualizes the intrinsic representation of an SSK when kernel parameters are purposely chosen to provide a bad fit.

We consider the classical problem of finding the minimum feedback arc set on tournaments (MFAST).

Furthermore, weprove information theoretic lower bounds which establish minimax optimality of the skillparameter estimation technique usedinouralgorithm. These bounds utilize a continuum version of Fano's method along with a careful covering argument.

In this paper, we study the problem of learning the skill distribution of a population of agents from observations of pairwise games in a tournament. These games are played among randomly drawn agents from the population. The agents in our model can be individuals, sports teams, or Wall Street fund managers. Formally, we postulate that the likelihoods of outcomes of games are governed by the parametric Bradley-Terry-Luce (or multinomial logit) model, where the probability of an agent beating another is the ratio between its skill level and the pairwise sum of skill levels, and the skill parameters are drawn from an unknown, non-parametric skill density of interest. The problem is, in essence, to learn a distribution from noisy, quantized observations.

Automated negotiation has emerged as a critical area of research in multiagent systems, with applications spanning e-commerce, resource allocation, and autonomous decision-making. This paper presents ChargingBoul, a negotiating agent that competed in the 2022 Automated Negotiating Agents Competition (ANAC) and placed second in individual utility by an exceptionally narrow margin. ChargingBoul employs a lightweight yet effective strategy that balances concession and opponent modeling to achieve high negotiation outcomes. The agent classifies opponents based on bid patterns, dynamically adjusts its bidding strategy, and applies a concession policy in later negotiation stages to maximize utility while fostering agreements. We evaluate ChargingBoul's performance using competition results and subsequent studies that have utilized the agent in negotiation research. Our analysis highlights ChargingBoul's effectiveness across diverse opponent strategies and its contributions to advancing automated negotiation techniques. We also discuss potential enhancements, including more sophisticated opponent modeling and adaptive bidding heuristics, to improve its performance further.