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1. How barriers become invisible: Children are less sensitive to constraints that are stable over time

   https://doi.org/10.1111/desc.13496  

   Amemiya, Jamie; Heyman, Gail D; Walker, Caren M (July 2024, Developmental Science)

   Abstract When making inferences about the mental lives of others (e.g., others’ preferences), it is critical to consider the extent to which the choices we observe are constrained. Prior research on the development of this tendency indicates a contradictory pattern: Children show remarkable sensitivity to constraints in traditional experimental paradigms, yet often fail to consider real‐world constraints and privilege inherent causes instead. We propose that one explanation for this discrepancy may be that real‐world constraints are often stable over time and lose their salience. The present research tested whether children (N = 133, 5‐ to 12‐year‐old mostly US children; 55% female, 45% male) becomelesssensitive to an actor's constraints after first observing two constrained actors (Stable condition) versus after first observing two actors in contexts with greater choice (Not Stable condition). We crossed thestabilityof the constraint with thetypeof constraint: either the constraint was deterministic such that there was only one option available (No Other Option constraint) or, in line with many real‐world constraints, the constraint was probabilistic such that therewasanother option, but it was difficult to access (Hard to Access constraint). Results indicated that children in the Stable condition became less sensitive to the probabilistic Hard to Access constraint across trials. Notably, we also found that children's sensitivity to constraints was enhanced in the Not Stable condition regardless of whether the constraint was probabilistic or deterministic. We discuss implications for children's sensitivity to real‐world constraints. Research HighlightsThis research addresses the apparent contradiction that children are sensitive to constraints in experimental paradigms but are ofteninsensitiveto constraints in the real world.One explanation for this discrepancy is that constraints in the real world tend to be stable over time and may lose their salience.When probabilistic constraints (i.e., when a second option is available but hard to access) are stable, children become de‐sensitized to constraints across trials.First observing contexts with greater choice increases children's sensitivity to both probabilistic and deterministic constraints. 

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2. Causal Constraints in the Life and Social Sciences

   https://doi.org/10.1017/psa.2023.165  

   Ross, Lauren N. (December 2023, Philosophy of Science)

   Abstract This paper examines constraints and their role in scientific explanation. Common views in the philosophical literature suggest that constraints are non-causal and that they provide non-causal explanations. While much of this work focuses on examples from physics, this paper explores constraints from other fields, including neuroscience, physiology, and the social sciences. I argue that these cases involve constraints that are causal and that provide a unique type of causal explanation. This paper clarifies what it means for a factor to be a constraint, when such constraints are causal, and how they figure in scientific explanation. 

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3. Simulating domain architecture evolution

   https://doi.org/10.1093/bioinformatics/btac242  

   Cui, Xiaoyue; Xue, Yifan; McCormack, Collin; Garces, Alejandro; Rachman, Thomas_W; Yi, Yang; Stolzer, Maureen; Durand, Dannie (June 2022, Bioinformatics)

   Abstract MotivationSimulation is an essential technique for generating biomolecular data with a ‘known’ history for use in validating phylogenetic inference and other evolutionary methods. On longer time scales, simulation supports investigations of equilibrium behavior and provides a formal framework for testing competing evolutionary hypotheses. Twenty years of molecular evolution research have produced a rich repertoire of simulation methods. However, current models do not capture the stringent constraints acting on the domain insertions, duplications, and deletions by which multidomain architectures evolve. Although these processes have the potential to generate any combination of domains, only a tiny fraction of possible domain combinations are observed in nature. Modeling these stringent constraints on domain order and co-occurrence is a fundamental challenge in domain architecture simulation that does not arise with sequence and gene family simulation. ResultsHere, we introduce a stochastic model of domain architecture evolution to simulate evolutionary trajectories that reflect the constraints on domain order and co-occurrence observed in nature. This framework is implemented in a novel domain architecture simulator, DomArchov, using the Metropolis–Hastings algorithm with data-driven transition probabilities. The use of a data-driven event module enables quick and easy redeployment of the simulator for use in different taxonomic and protein function contexts. Using empirical evaluation with metazoan datasets, we demonstrate that domain architectures simulated by DomArchov recapitulate properties of genuine domain architectures that reflect the constraints on domain order and adjacency seen in nature. This work expands the realm of evolutionary processes that are amenable to simulation. Availability and implementationDomArchov is written in Python 3 and is available at http://www.cs.cmu.edu/~durand/DomArchov. The data underlying this article are available via the same link. Supplementary informationSupplementary data are available at Bioinformatics online. 

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4. Safe reinforcement learning for dynamical games

   https://doi.org/10.1002/rnc.4962  

   Yang, Yongliang; Vamvoudakis, Kyriakos G.; Modares, Hamidreza (March 2020, International Journal of Robust and Nonlinear Control)

   Summary This article presents a novel actor‐critic‐barrier structure for the multiplayer safety‐critical systems. Non‐zero‐sum (NZS) games with full‐state constraints are first transformed into unconstrained NZS games using a barrier function. The barrier function is capable of dealing with both symmetric and asymmetric constraints on the state. It is shown that the Nash equilibrium of the unconstrained NZS guarantees to stabilize the original multiplayer system. The barrier function is combined with an actor‐critic structure to learn the Nash equilibrium solution in an online fashion. It is shown that integrating the barrier function with the actor‐critic structure guarantees that the constraints will not be violated during learning. Boundedness and stability of the closed‐loop signals are analyzed. The efficacy of the presented approach is finally demonstrated by using a simulation example. 

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5. Incorporating background knowledge in symbolic regression using a computer algebra system

   https://doi.org/10.1088/2632-2153/ad4a1e  

   Fox, Charles; Tran, Neil_D; Nacion, F_Nikki; Sharlin, Samiha; Josephson, Tyler_R (June 2024, Machine Learning: Science and Technology)

   Abstract Symbolic regression (SR) can generate interpretable, concise expressions that fit a given dataset, allowing for more human understanding of the structure than black-box approaches. The addition of background knowledge (in the form of symbolic mathematical constraints) allows for the generation of expressions that are meaningful with respect to theory while also being consistent with data. We specifically examine the addition of constraints to traditional genetic algorithm (GA) based SR (PySR) as well as a Markov-chain Monte Carlo (MCMC) based Bayesian SR architecture (Bayesian Machine Scientist), and apply these to rediscovering adsorption equations from experimental, historical datasets. We find that, while hard constraints prevent GA and MCMC SR from searching, soft constraints can lead to improved performance both in terms of search effectiveness and model meaningfulness, with computational costs increasing by about an order of magnitude. If the constraints do not correlate well with the dataset or expected models, they can hinder the search of expressions. We find incorporating these constraints in Bayesian SR (as the Bayesian prior) is better than by modifying the fitness function in the GA. 

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