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We investigate whether risk and time preferences differ when individuals make decisions for others compared to making decisions for themselves. We introduce a novel ``skin in the game'' experimental design, where choices for others incur a direct cost to the decision-maker, ensuring a genuine trade-off between self-interest and surrogate allocation. The modal outcome is that participants are more risk-averse and impatient when choosing for others than for themselves. Our methodology reveals significant heterogeneity, successfully identifying selfish types often missed by the more standard ``no skin in the game'' approaches. The message is nuanced, as even non-selfish participants behave differently when they have skin in the game. Furthermore, our framework yields more consistent behavior and superior out-of-sample predictive power.

We obtain results on cut and choose games for complete Boolean algebras. Zapletal proved that there is a Boolean algebra $\mathbb{B}$ such that $\mathcal{G}_ω^\textsf{candc}(\mathbb{B})$, the version of the game which ends on the $ω$'th round, is undetermined. We prove that, assuming the consistency of a proper class of supercompact cardinals, the limit version $\mathcal{G}^{\textsf{candc}}_{< λ}(\mathbb{B})$, in which there are $λ$-many rounds but no concluding round, is consistently determined for all complete Boolean algebras $\mathbb{B}$ and all successor cardinals $λ$. In particular, this answers a question of Zapletal \cite[Question 2]{Zapletal1995}. We also show that undetermined instances of the game $\mathcal{G}^\textsf{candc}_λ(\mathbb{B})$ follow from the approachability property, extending results of Dobrinen, and we prove that undetermined instances are compatible with $\textsf{MM}^{++}$.

In this work we continue the study of non-chaotic asymptotic correlations in many element systems and discuss the emergence of a new notion of asymptotic correlation -- partial order -- in the Choose the Leader (CL) system. Similarly to the newly defined notion of order, partial order refers to alignment of the elements in the system -- though it allows for deviation from total adherence. Our presented work revolves around the definition of partial order and shows its emergence in the CL model in its original critical scaling. Furthermore, we discuss the propagation of partial order in the CL model and give a quantitative estimate to the convergence to this state. This new notion (as well as that of order) opens the door to exploring old and new (probabilistic) models of biological and societal nature in a more realistic way.

Based on the existing literature, this article presents the different ways of choosing the parameters of stochastic volatility models in general, in the context of pricing financial derivative contracts. This includes the use of stochastic volatility inside stochastic local volatility models.

In digital markets comprised of many competing services, each user chooses between multiple service providers according to their preferences, and the chosen service makes use of the user data to incrementally improve its model. The service providers' models influence which service the user will choose at the next time step, and the user's choice, in return, influences the model update, leading to a feedback loop. In this paper, we formalize the above dynamics and develop a simple and efficient decentralized algorithm to locally minimize the overall user loss. Theoretically, we show that our algorithm asymptotically converges to stationary points of of the overall loss almost surely. We also experimentally demonstrate the utility of our algorithm with real world data.

The field of reinforcement learning offers a large variety of concepts and methods to tackle sequential decision-making problems. This variety has become so large that choosing an algorithm for a task at hand can be challenging. In this work, we streamline the process of choosing reinforcement-learning algorithms and action-distribution families. We provide a structured overview of existing methods and their properties, as well as guidelines for when to choose which methods. An interactive version of these guidelines is available online at https://rl-picker.github.io/.

The Langevin equation is a common tool to model diffusion at a single-particle level. In non-homogeneous environments, such as aqueous two-phase systems or biological condensates with different diffusion coefficients in different phases, the solution to a Langevin equation is not unique unless the interpretation of stochastic integrals involved is selected. We analyze the diffusion of particles in such systems and evaluate the mean, the mean square displacement, and the distribution of particles, as well as the variance of the time-averaged mean-squared displacements. Our analytical results provide a method to choose the interpretation parameter from single particle tracking experiments.

It is well known that the variable ordering can be critical to the efficiency or even tractability of the cylindrical algebraic decomposition (CAD) algorithm. We propose new heuristics inspired by complexity analysis of CAD to choose the variable ordering. These heuristics are evaluated against existing heuristics with experiments on the SMT-LIB benchmarks using both existing performance metrics and a new metric we propose for the problem at hand. The best of these new heuristics chooses orderings that lead to timings on average 17% slower than the virtual-best: an improvement compared to the prior state-of-the-art which achieved timings 25% slower.

The identity j/n {kn}\choose{n+j} =(k-1) {kn-1}\choose{n+j-1}- {kn-1}\choose{n+j} shows that j/n {kn}\choose{n+j} is always an integer. Here we give a combinatorial interpretation of this integer in terms of lattice paths, using a uniformly distributed statistic. In particular, the case j=1,k=2 gives yet another manifestation of the Catalan numbers.

A violation of Bell-CHSH inequalities does not justify speculations about quantum non-locality, conspiracy and retro-causation. Such speculations are rooted in a belief that setting dependence of hidden variables in a probabilistic model, called a violation of measurement independence, would mean a violation of experimenters freedom of choice. This belief is unfounded because it is based on a questionable use of Bayes Theorem and on incorrect causal interpretation of conditional probabilities. In Bell-local realistic model, hidden variables describe only photonic beams created by a source, thus they cannot depend on randomly chosen experimental settings. However, if hidden variables describing measuring instruments are correctly incorporated into a contextual probabilistic model a violation of inequalities and an apparent violation of no-signaling reported in Bell tests can be explained without evoking quantum nonlocality. Therefore, for us, a violation of Bell-CHSH inequalities proves only that hidden variables have to depend on settings confirming contextual character of quantum observables and an active role played by measuring instruments. Bell thought that he had to choose bet

We describe a two-stage mechanism that fully implements the set of efficient outcomes in two-agent environments with quasi-linear utilities. The mechanism asks one agent to set prices for each outcome, and the other agent to make a choice, paying the corresponding price: Price \& Choose. We extend our implementation result in three main directions: an arbitrary number of players, non-quasi linear utilities, and robustness to max-min behavior. Finally, we discuss how to reduce the payoff inequality between players while still achieving efficiency.

Research processes often rely on high-performance computing (HPC), but HPC is often seen as antithetical to "reproducibility": one would have to choose between software that achieves high performance, and software that can be deployed in a reproducible fashion. However, by giving up on reproducibility we would give up on verifiability, a foundation of the scientific process. How can we conciliate performance and reproducibility? This article looks at two performance-critical aspects in HPC: message passing (MPI) and CPU micro-architecture tuning. Engineering work that has gone into performance portability has already proved fruitful, but some areas remain unaddressed when it comes to CPU tuning. We propose package multi-versioning, a technique developed for GNU Guix, a tool for reproducible software deployment, and show that it allows us to implement CPU tuning without compromising on reproducibility and provenance tracking.

The transferability of adversarial examples is a key issue in the security of deep neural networks. The possibility of an adversarial example crafted for a source model fooling another targeted model makes the threat of adversarial attacks more realistic. Measuring transferability is a crucial problem, but the Attack Success Rate alone does not provide a sound evaluation. This paper proposes a new methodology for evaluating transferability by putting distortion in a central position. This new tool shows that transferable attacks may perform far worse than a black box attack if the attacker randomly picks the source model. To address this issue, we propose a new selection mechanism, called FiT, which aims at choosing the best source model with only a few preliminary queries to the target. Our experimental results show that FiT is highly effective at selecting the best source model for multiple scenarios such as single-model attacks, ensemble-model attacks and multiple attacks (Code available at: https://github.com/t-maho/transferability_measure_fit).

We investigate a variety of cut and choose games, their relationship with (generic) large cardinals, and show that they can be used to characterize a number of properties of ideals and of partial orders: certain notions of distributivity, strategic closure, and precipitousness.

A major challenge when using k-means clustering often is how to choose the parameter k, the number of clusters. In this letter, we want to point out that it is very easy to draw poor conclusions from a common heuristic, the "elbow method". Better alternatives have been known in literature for a long time, and we want to draw attention to some of these easy to use options, that often perform better. This letter is a call to stop using the elbow method altogether, because it severely lacks theoretic support, and we want to encourage educators to discuss the problems of the method -- if introducing it in class at all -- and teach alternatives instead, while researchers and reviewers should reject conclusions drawn from the elbow method.

This question is raised by Cason, Friedman and Hopkins (CFH, 2012) after they firstly found and indexed quantitatively the cycles in a continuous time experiment. To answer this question, we use the data from standard RPS experiment. Our experiments are of the traditional setting - in each of repeated rounds, the subjects are paired with random matching, using pure strategy and must choose simultaneously, and after each round, each subject obtains only private information. This economics environment is a decartelized and low-information one. Using the cycle rotation indexes (CRI, developed by CFH) method, we find, the cycles not only exist but also persist in our experiment. Meanwhile, the cycles' direction are consistent with 'standard' learning models. That is the answer to the CHF question: Cycles do not dissipate in the simultaneously choose game. In addtion, we discuss three questions (1) why significant cycles are uneasy to be obtained in traditional setting experiments; (2) why CRI can be an iconic indexing-method for 'standard' evolution dynamics; and (3) where more cycles could be expected.

Dynamic network data are now available in a wide range of contexts and domains. Several representation formalisms exist to represent dynamic networks, but there is no well-known method to choose one representation over another for a given dataset. In this article, we propose a method based on data compression to choose between three of the most important representations: snapshots, link streams and interval graphs. We apply the method on synthetic and real datasets to show the relevance of the method and its possible applications, such as choosing an appropriate representation when confronted to a new dataset, and storing dynamic networks in an efficient manner.