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Airtankers are commonly used for initial attack (IA) to reduce the likelihood of wildland fires escaping containment efforts. We examined IA airtanker dispatch decisions for forest fires in Ontario, Canada, through an analysis of historical fire records from 2001 to 2019. A hurdle modelling approach that predicts the probability of airtanker(s) being dispatched and then the number of airtankers sent was used. Two different hurdle models were considered depending on the timing of the information available to the decision maker: a 'fire report model' based upon the information available when a fire is first reported, and an 'initial attack model' using information available at the time IA action began. Both models indicated that the most influential covariates for airtanker dispatch are the fire weather index, fuel volatility, observed fire rate of spread, fire size at IA, and cause of ignition. When evaluating the predictive ability of the models on a validation data set, the 'initial attack model' performed better than the 'fire report model'. Our models generally perform well when predicting none, one, or two airtankers on IA, but they generally underpredict when more than two airtankers are dispatched, which suggests risk-averse decisionmaking in fire management resource dispatching. Key words: aerial suppression, air attack, fire management, fire suppression, hurdle model, fire weather indices, 1. Introduction Forest fires are a natural ecological process, which provide many benefits to forest health. The ecological role of fire is encouraged on the landscape through managed fire in [...]

Behavior analysts make dozens of practice-related decisions every day. Past research has extensively examined practicerelated decision making by medical and other healthcare professionals, but decision making by practicing behavior analysts has garnered little research attention. The purpose of this proof of concept study was to begin a translational research agenda toward understanding what variables influence behavior analyst decision making and precisely how those variables do so. Behavior analyst decision making often involves data and the most fundamental characteristics of data are those indicating data trustworthiness--validity, reliability, and accuracy. To isolate a single independent variable, we used a lengthening data path procedure to parametrically assess how reducing data accuracy changed decisions to continue or modify an intervention in 30 students of behavior-analytic masters or doctoral programs. When data accuracy was 100%, most participants waited 9-10 trials before intervening. When data accuracy was below 60%, most participants waited 4-6 trials before intervening. To begin exploring potential behavioral processes underlying the influence of data accuracy on practice-related decisions, we also examined how well one popular description of decision making described participant choice--probability discounting. Probability discounting described the pattern of choices well for 16 of the 30 participants, suggesting other analytic frameworks should be explored. Nevertheless, data accuracy systematically influenced the majority of participants choices to continue or modify an intervention, although the degree of influence differed on an individual basis. Keywords visual analysis * choice * probability discounting * decision making, Introduction To extend Hermstein (1970), all practice-related decisions in applied behavior analysis (ABA) are choice. For every decision a behavior analyst makes, there are likely one or more responses the [...]

We present the fundamentals of the quantum theoretical approach we have developed in the last decade to model cognitive phenomena that resisted modeling by means of classical logical and probabilistic structures, like Boolean, Kolmogorovian and, more generally, set theoretical structures. We firstly sketch the operational-realistic foundations of conceptual entities, i.e. concepts, conceptual combinations, propositions, decision-making entities, etc. Then, we briefly illustrate the application of the quantum formalism in Hilbert space to represent combinations of natural concepts, discussing its success in modeling a wide range of empirical data on concepts and their conjunction, disjunction and negation. Next, we naturally extend the quantum theoretical approach to model some long-standing 'fallacies of human reasoning', namely, the 'conjunction fallacy' and the 'disjunction effect'. Finally, we put forward an explanatory hypothesis according to which human reasoning is a defined superposition of 'emergent reasoning' and 'logical reasoning', where the former generally prevails over the latter. The quantum theoretical approach explains human fallacies as the consequence of genuine quantum structures in human reasoning, i.e. 'contextuality', 'emergence', 'entanglement', 'interference' and 'superposition'. As such, it is alternative to the Kahneman-Tversky research programme, which instead aims to explain human fallacies in terms of 'individual heuristics and biases'., Author(s): Diederik Aerts [sup.1], Massimiliano Sassoli de Bianchi [sup.2] [sup.3], Sandro Sozzo [sup.4], Tomas Veloz [sup.5] [sup.6] Author Affiliations: (1) grid.8767.e, 0000 0001 2290 8069, Department of Mathematics, Center Leo [...]

Exogenous Learning Based on Economic Incentives A learning curve is a graphical representation of the cost of producing an item against the number of items produced over time. In 1936, [...]

Behavior analysts visually analyze graphs to interpret data in order to make data-based decisions. Though front-line employees implement behavioral interventions on a daily basis, they are not often trained to interpret these data. A clinical decision-making model may aid front-line employees in learning how to interpret graphs. A multiple-baseline-across-participants design was used to evaluate the effectiveness of a clinical decision-making model on the percentage of correct responses when interpreting line graphs. All of the participants increased their percentage of correct responses after the introduction of the clinical decision-making model. Two of the eight participants required additional feedback. The implications of these findings are discussed., Author(s): Kailie J. Kipfmiller [sup.1] , Matthew T. Brodhead [sup.1] , Katie Wolfe [sup.2] , Kate LaLonde [sup.1] , Emma S. Sipila [sup.1] , M. Y. Savana Bak [sup.1] , [...]

Abstract. The paper develops a model for combining point forecasts into a predictive distribution for a variable of interest. Our approach allows for point forecasts to be correlated and admits [...]

Abstract. In the Anscombe-Aumann setup, we provide conditions for a collection of observations to be consistent with a well-known class of smooth ambiguity preferences (Klibanoff P, Marinacci M, Mukerji S [...]

We propose empirical dynamic programming algorithms for Markov decision processes. In these algorithms, the exact expectation in the Bellman operator in classical value iteration is replaced by an empirical estimate [...]

We propose a two-stage risk-averse stochastic optimization problem with a stochastic-order constraint on a vector-valued function of the second-stage decisions. This model is motivated by a multiobjective second-stage problem. We [...]

We provide an empirical and theoretical assessment of the value of information sharing in a two-stage supply chain. The value of downstream sales information to the upstream firm stems from improving upstream order fulfillment forecast accuracy. Such an improvement can lead to lower safety stock and better service. Based on the data collected from a consumer packaged goods company, we empirically show that, if the company includes the downstream sales data to forecast orders, the improvement in the mean squared forecast error ranges from 7.1% to 81.1% across all studied products. Theoretical models in the literature, however, suggest that the value of information sharing should be zero for over half of our studied products. To reconcile the gap between the literature and the empirical observations, we develop a new theoretical model. Whereas the literature assumes that the decision maker strictly adheres to a given inventory policy, our model allows him to deviate, accounting for private information held by the decision maker, yet unobservable to the econometrician. This turns out to reconcile our empirical findings with the literature. These 'decision deviations' lead to information losses in the order process, resulting in a strictly positive value of downstream information sharing. Furthermore, we empirically quantify and show the significance of the value of operations knowledge--the value of knowing the downstream replenishment policy. Keywords: supply chain; information sharing; signal propagation; decision deviation; time series; empirical forecasting; autoregressive integrated moving average process History. Received April 9, 2013; accepted November 25, 2014, by Serguei Netessine, operations management. Published online in Articles in Advance June 29, 2015., 1. Introduction The abundance of information technology has had a massive impact on supply chain coordination. Sharing downstream demand information with upstream suppliers has improved supply chain performance in practice. [...]

Background: Impulsivity is understood as a range of behaviours, but the association between these behaviours is not well understood. Although high motor impulsivity is a key symptom of disorders like pathological gambling and addiction, in which decision-making on laboratory tasks is compromised, there have been no clear demonstrations that choice and motor impulsivity are associated in the general population. We examined this association in a large population of rodents. Methods: We performed a meta-analysis on behavioural data from 211 manipulation-naive male animals that performed a rodent gambling task in our laboratory between 2008 and 2012. The task measures an aspect of both impulsive decision-making and impulsive action, making it possible to evaluate whether these 2 forms of maladaptive behaviour are related. Results: Our meta-analysis revealed that motor impulsivity was positively correlated with poor decisionmaking under risk. Highly motor impulsive rats were slower to adopt an advantageous choice strategy and quicker to make a choice on individual trials. Limitations: The data analyzed were limited to that produced by our laboratory and did not include data of other researchers who have used the task. Conclusion: This work may represent the first demonstration of a clear association between choice and motor impulsivity in a nonclinical population. This lends support to the common practice of studying impulsivity in nonclinical populations to gain insight into impulse control disorders and suggests that differences in impulsive behaviours between clinical and nonclinical populations may be ones of magnitude rather than ones of quality., Introduction Impulsivity, loosely defined as action without foresight, is a component of many psychiatric disorders, including attentiondeficit/hyperactivity disorder, mania, substance use and addictive disorders, and impulse control disorders (ICDs). (1) [...]

Background: Few reports have evaluated prognostic modelling studies of tools used for surgical decision-making. This systematic review aimed to describe and critically appraise studies that have developed or validated multivariable [...]