Optimal decision making and matching are tied through diminishing returns

Matching provides efficient decisions

How humans and animals distribute their behavior across choice options has been of key interest to economics, psychology, ecology, and related fields. Neoclassical and behavioral economics have provided prescriptions for how decision-makers can maximize their reward or utility, but these formalisms are used by decision-makers rarely. Instead, individuals allocate their behavior in proportion to the worth of their options, a phenomenon captured by the generalized matching law. Why biological decision-makers adopt this strategy has been unclear. To provide insight into this issue, this article evaluates the performance of matching across a broad spectrum of decision situations, using simulations. Matching is found to attain a high or near-optimal gain, and the strategy achieves this level of performance following a single evaluation of the decision options. Thus, matching provides highly efficient decisions across a wide range of choice environments. This result offers a quantitative explanation for the broad adoption of matching by biological decision-makers.

Matching provides efficient decisions

How humans and animals distribute their behavior across choice options has been of key interest to economics, psychology, ecology, and related fields. Neoclassical and behavioral economics have provided prescriptions for how decision-makers can maximize their reward or utility, but these formalisms are used by decision-makers rarely. Instead, individuals allocate their behavior in proportion to the worth of their options, a phenomenon captured by the generalized matching law. Why biological decision-makers adopt this strategy has been unclear. To provide insight into this issue, this article evaluates the performance of matching across a broad spectrum of decision situations, using simulations. Matching is found to attain a high or near-optimal gain, and the strategy achieves this level of performance following a single evaluation of the decision options. Thus, matching provides highly efficient decisions across a wide range of choice environments. This result offers a quantitative explanation for the broad adoption of matching by biological decision-makers.

Sociodemographics Impact Competitiveness in the Neurosurgical Match: Survey Results

BACKGROUND

The neurosurgical match involves selecting future neurosurgeons who will comprise the future national workforce, based on a competitive ranking process of applicants. We aimed to identify which sociodemographic and academic factors influence competitiveness and rank position in the match.

METHODS

A Council of State Neurosurgical Societies (CSNS) survey was distributed to current U.S. neurosurgical residents. The primary outcome measure was self-reported rank position of matched program. Variables included sociodemographic and academic metrics. Fisher exact, logistic regression, and t tests were performed.

RESULTS

Among the 72 respondents, median United States Medical Licensing Examination Step 1 score was 248, 34.7% were Alpha Omega Alpha inductees, 77.8% completed 1-3 sub-internship rotations, median number of publications was 5, and 13.9% had a Ph.D. Sociodemographic analysis demonstrated that 69.4% were male and 30.6% were female. Applicants with a home neurosurgery program or of female gender had statistically significantly higher odds of matching into a top 3 program on their rank list (odds ratio = 9 and odds ratio = 6, respectively). Female applicants exhibited similar mean, but less variance, compared with male respondents for United States Medical Licensing Examination Step 1 scores and number of publications. Respondents with a top 3 program match were more likely to agree that the home program supported their pursuance of neurosurgery.

CONCLUSIONS

Two sociodemographic factors were independently associated with high match rank: presence of home neurosurgery program and female gender. Female respondents reported consistently strong academic metrics (similar mean, but less variance, compared with male respondents).

Developmental feedbacks and the emergence of individuality

Behavioural individuality is a hallmark of animal life, with major consequences for fitness, ecology, and evolution. One of the most widely invoked explanations for this variation is that feedback loops between an animal's behaviour and its state (e.g. physiology, informational state, social rank, etc.) trigger and shape the development of individuality. Despite their often-cited importance, however, little is known about the ultimate causes of such feedbacks. Expanding on a previously employed model of adaptive behavioural development under uncertainty, we find that (i) behaviour-state feedbacks emerge as a direct consequence of adaptive behavioural development in particular selective environments and (ii) that the sign of these feedbacks, and thus the consequences for the development of behavioural individuality, can be directly predicted by the shape of the fitness function, with increasing fitness benefits giving rise to positive feedbacks and trait divergence and decreasing fitness benefits leading to negative feedbacks and trait convergence. Our findings provide a testable explanatory framework for the emergence of developmental feedbacks driving individuality and suggest that such feedbacks and their associated patterns of behavioural diversity are a direct consequence of adaptive behavioural development in particular selective environments.

Developmental feedbacks and the emergence of individuality

Behavioural individuality is a hallmark of animal life, with major consequences for fitness, ecology, and evolution. One of the most widely invoked explanations for this variation is that feedback loops between an animal's behaviour and its state (e.g. physiology, informational state, social rank, etc.) trigger and shape the development of individuality. Despite their often-cited importance, however, little is known about the ultimate causes of such feedbacks. Expanding on a previously employed model of adaptive behavioural development under uncertainty, we find that (i) behaviour-state feedbacks emerge as a direct consequence of adaptive behavioural development in particular selective environments and (ii) that the sign of these feedbacks, and thus the consequences for the development of behavioural individuality, can be directly predicted by the shape of the fitness function, with increasing fitness benefits giving rise to positive feedbacks and trait divergence and decreasing fitness benefits leading to negative feedbacks and trait convergence. Our findings provide a testable explanatory framework for the emergence of developmental feedbacks driving individuality and suggest that such feedbacks and their associated patterns of behavioural diversity are a direct consequence of adaptive behavioural development in particular selective environments.

Developmental feedbacks and the emergence of individuality

Behavioural individuality is a hallmark of animal life, with major consequences for fitness, ecology, and evolution. One of the most widely invoked explanations for this variation is that feedback loops between an animal's behaviour and its state (e.g. physiology, informational state, social rank, etc.) trigger and shape the development of individuality. Despite their often-cited importance, however, little is known about the ultimate causes of such feedbacks. Expanding on a previously employed model of adaptive behavioural development under uncertainty, we find that (i) behaviour-state feedbacks emerge as a direct consequence of adaptive behavioural development in particular selective environments and (ii) that the sign of these feedbacks, and thus the consequences for the development of behavioural individuality, can be directly predicted by the shape of the fitness function, with increasing fitness benefits giving rise to positive feedbacks and trait divergence and decreasing fitness benefits leading to negative feedbacks and trait convergence. Our findings provide a testable explanatory framework for the emergence of developmental feedbacks driving individuality and suggest that such feedbacks and their associated patterns of behavioural diversity are a direct consequence of adaptive behavioural development in particular selective environments.

Density Management Is More Cost Effective than Fertilization for Chimonobambusa pachystachys Bamboo-Shoot Yield and Economic Benefits

Stand-density management and fertilization practices are the main two factors affecting bamboo-shoot yield. However, the appropriate density and fertilization rates are still unclear for improving the bamboo-shoot yield and its economic benefits, especially for a high economic value bamboo-shoot forest. To fill this gap, we conducted a two-year split-plot design experiment in a Chimonobambusa pachystachys shoot forest. The main plots were assigned to five density rates, 40,000, 50,000, 60,000, 70,000, and 100,000 culms ha−1, and the subplots were assigned to four fertilization rates (nitrogen:phosphorus:potassium = 23:3:15): 0, 820, 1640, and 2460 kg ha−1 a−1. Results showed that the bamboo-shoot yield increased first and then decreased with stand density, while it increased with fertilization rates. Density management and fertilization regulate bamboo-shoot yield by changing the soil’s Olsen P, available nitrogen, organic matter, and available potassium contents. The maximum bamboo-shoot yield was 9315.92 kg ha−1, which appeared in the density of 60,000 culms ha−1 and the fertilization of 2460 kg ha−1 a−1. However, the maximum bamboo-shoot net profit was 135,242.63 CNY ha−1, which appeared at the density of 60,000 culms ha−1 and the fertilization of 1640 kg ha−1 a−1. The economic-benefit analysis shows that density management achieves a net-profit growth comparable to fertilizer application at a much lower cost. The study results provide a basis for the scientific management of C. pachystachys shoot forests and bamboo farmers to improve their income.

Matching Behaviours and Rewards

Matching describes how behaviour is related to rewards. The matching law holds when the ratio of an individual's behaviours equals the ratio of the rewards obtained. From its origins in the study of pigeons working for food in the laboratory, the law has been applied to a range of species, both in the laboratory and outside it (e.g., human sporting decisions). Probability matching occurs when the probability of a behaviour equals the probability of being rewarded. Input matching predicts the distribution of individuals across habitats. We evaluate the rationality of the matching law and probability matching, expose the logic of matching in real-world cases, review how recent neuroscience findings relate to matching, and suggest future research directions.

Reward foraging task and model-based analysis reveal how fruit flies learn value of available options

Foraging animals have to evaluate, compare and select food patches in order to increase their fitness. Understanding what drives foraging decisions requires careful manipulation of the value of alternative options while monitoring animals choices. Value-based decision-making tasks in combination with formal learning models have provided both an experimental and theoretical framework to study foraging decisions in lab settings. While these approaches were successfully used in the past to understand what drives choices in mammals, very little work has been done on fruit flies. This is despite the fact that fruit flies have served as model organism for many complex behavioural paradigms. To fill this gap we developed a single-animal, trial-based decision making task, where freely walking flies experienced optogenetic sugar-receptor neuron stimulation. We controlled the value of available options by manipulating the probabilities of optogenetic stimulation. We show that flies integrate reward history of chosen options and forget value of unchosen options. We further discover that flies assign higher values to rewards experienced early in the behavioural session, consistent with formal reinforcement learning models. Finally, we also show that the probabilistic rewards affect walking trajectories of flies, suggesting that accumulated value is controlling the navigation vector of flies in a graded fashion. These findings establish the fruit fly as a model organism to explore the genetic and circuit basis of reward foraging decisions.

On the Distinction Between Personal Standards Perfectionism and Excellencism: A Theory Elaboration and Research Agenda

Research on perfectionism is flourishing, but the unspecified distinction between perfectionism and the pursuit of excellence is a lingering issue that urgently needs conceptual, theoretical, and empirical attention. In this article, excellence and perfection are defined as distinct goals that form the basis of two different but related constructs. To move this idea forward, the term excellencism is introduced. Perfectionism and excellencism are defined and their similarities and differences are illustrated using symbolic logic and adjectives from the English lexicon. A point is made to clearly indicate that excellencism is a required reference point for reassessing the healthiness or unhealthiness of personal standards perfectionism. Using the law of diminishing returns as an analogy, a theory-driven rationale is proposed, and three alternative hypotheses are formulated. Showing that personal standards perfectionism is associated with better, comparable, and worse outcomes compared with excellencism offers the needed and sufficient conditions for respectively supporting the hypothesis that perfectionism is a healthy, unneeded, or deleterious pursuit. The propositions advanced in this theoretical article are more than incremental, and their practical implications are far-reaching: If personal standards perfectionism yields no added value or deleterious outcomes over and above excellencism, then excellence rather than perfection should be promoted.

Optimal response vigor and choice under non-stationary outcome values

Within a rational framework, a decision-maker selects actions based on the reward-maximization principle, which stipulates that they acquire outcomes with the highest value at the lowest cost. Action selection can be divided into two dimensions: selecting an action from various alternatives, and choosing its vigor, i.e., how fast the selected action should be executed. Both of these dimensions depend on the values of outcomes, which are often affected as more outcomes are consumed together with their associated actions. Despite this, previous research has only addressed the computational substrate of optimal actions in the specific condition that the values of outcomes are constant. It is not known what actions are optimal when the values of outcomes are non-stationary. Here, based on an optimal control framework, we derive a computational model for optimal actions when outcome values are non-stationary. The results imply that, even when the values of outcomes are changing, the optimal response rate is constant rather than decreasing. This finding shows that, in contrast to previous theories, commonly observed changes in action rate cannot be attributed solely to changes in outcome value. We then prove that this observation can be explained based on uncertainty about temporal horizons; e.g., the session duration. We further show that, when multiple outcomes are available, the model explains probability matching as well as maximization strategies. The model therefore provides a quantitative analysis of optimal action and explicit predictions for future testing.

Applying the matching law as micro-foundation of social phenomena

Social phenomena are suggested to be explained by the matching law - an empirical regularity of individual behaviour. While a considerable amount of psychological research on this law exists, only a few sociological applications can be found. This paper points to the problems that come with its usage as micro-foundation of social behaviour and provides solutions. In particular, a model of melioration learning enables the derivation of social phenomena from the matching law. The proposed approach is illustrated by the application of the learning model to the volunteer's dilemma. In contrast to game-theoretical solutions, the matching law leads to more intuitive results in case of the asymmetric dilemma. The relationship between the matching law and utility maximisation is discussed by its integration into economic consumer theory.