A characterization of sequential rationalizability

A choice function is sequentially rationalizable if there is an ordered collection of asymmetric binary relations that identifies the selected alternative in every choice problem. We propose a property, F-consistency, and show that it characterizes the notion of sequential rationalizability. F-consistency is a testable property that highlights the behavioral aspects implicit in sequentially rationalizable choice. Further, our characterization result provides a novel tool with which to study how other behavioral concepts are related to sequential rationalizability, and establish a priori unexpected implications. In particular, we show that the concept of rationalizability by game trees, which, in principle, had little to do with sequential rationalizability, is a refinement of the latter. Every choice function that is rationalizable by a game tree is also sequentially rationalizable. Finally, we show that some prominent voting mechanisms are also sequentially rationalizable.Individual rationality, Rationalizability, Consistency, Bounded rationality, Behavioral economics, Voting

Welfare of naive and sophisticated players in school choice

Two main school choice mechanisms have attracted the attention in the literature: Boston and deferred acceptance (DA). The question arises on the ex-ante welfare implications when the game is played by participants that vary in terms of their strategic sophistication. Abdulkadiroglu, Che and Yasuda (2011) have shown that the chances of naive participants getting into a good school are higher under the Boston mechanism than under DA, and some naive participants are actually better off. In this note we show that these results can be extended to show that, under the veil of ignorance, i.e. students not yet knowing their utility values, all naive students may prefer to adopt the Boston mechanism.School Choice; Naive Players; Welfare; Veil of Ignorance

A theory of reference-dependent behavior

Extensive field and experimental evidence in a variety of environments show that behavior depends on a reference point. This paper provides an axiomatic characterization of this dependence. We proceed by imposing gradually more structure on both choice correspondences and preference relations, requiring increasingly higher levels of rationality, and freeing the decision-maker from certain types of inconsistencies. The appropriate degree of behavioral structure will depend on the phenomenon that is to be modeled. Lastly, we provide two applications of our work: one to model the status-quo bias, and another to model addictive behavior.Individual rationality, reference-dependence, rationalization, path independence, status-quo bias, addiction, habit formation, LeeX

On the complexity of rationalizing behavior

We study the complexity of rationalizing choice behavior. We do so by analyzing two polar cases, and a number of intermediate ones. In our most structured case, that is where choice behavior is defined in universal choice domains and satisfies the "weak axiom of revealed preference," finding the complete preorder rationalizing choice behavior is a simple matter. In the polar case, where no restriction whatsoever is imposed, either on choice behavior or on choice domain, finding the complete preorders that rationalize behavior turns out to be intractable. We show that the task of finding the rationalizing complete preorders is equivalent to a graph problem. This allows the search for existing algorithms in the graph theory literature, for the rationalization of choice.Rationalization, Computational complexity, NP-complete, Arbitrary Choice Domains

Muscular tension significantly affects stability in standing posture

Muscular co-contraction is a strategy commonly used by elders with the aim to increase stability. However, co-contraction leads to stiffness which in turns reduces stability. Some literature seems to suggest an opposite approach and to point out relaxation as a way to improve stability. Teaching relaxation is therefore becoming the aim of many studies letting unclear whether tension or relaxation are the most effective muscular strategy to improve stability. Relaxation is a misleading concept in our society. It is often confused with rest, while it should be addressed during stressing tasks, where it should aim to reduce energetic costs and increase stability. The inability to relax can be related to sub-optimal neuro-motor control, which can lead to increased stresses. Research question The objective of the study is to investigate the effect of voluntary muscle contraction and relaxation over the stability of human standing posture, answering two specific research questions: (1) Does the muscular tension have an impact on stability of standing posture? (2) Could this impact be estimated by using a minimally invasive procedure? Methods By using a force plate, we analysed the displacement of the center of pressure of 30 volunteers during state of tension and relaxation in comparison with a control state, and with open and closed eyes. Results We found that tension significantly reduced the stability of subjects (15 out of 16 parameters, p¿<¿0.003). Significance Our results show that daily situations of stress can lead to decreased stability. Such a loss might actually increase the risk of chronic joint overload or fall. Finally, breathing has direct effect over the management of pain and stress, and the results reported here point out the need to explicitly explore the troubling fact that a large portion of population might not be able to properly breath.Peer ReviewedPostprint (published version

Exploring the Potential of Robot-Collected Data for Training Gesture Classification Systems

Sensors and Artificial Intelligence (AI) have revolutionized the analysis of human movement, but the scarcity of specific samples presents a significant challenge in training intelligent systems, particularly in the context of diagnosing neurodegenerative diseases. This study investigates the feasibility of utilizing robot-collected data to train classification systems traditionally trained with human-collected data. As a proof of concept, we recorded a database of numeric characters using an ABB robotic arm and an Apple Watch. We compare the classification performance of the trained systems using both human-recorded and robot-recorded data. Our primary objective is to determine the potential for accurate identification of human numeric characters wearing a smartwatch using robotic movement as training data. The findings of this study offer valuable insights into the feasibility of using robot-collected data for training classification systems. This research holds broad implications across various domains that require reliable identification, particularly in scenarios where access to human-specific data is limited