Explore the vast range of titles available.

Decision makers often face choices between smaller more immediate rewards and larger more delayed rewards. For example, when foraging for food, animals must choose between actions that have varying costs (e.g., effort, duration, energy expenditure) and varying benefits (e.g., amount of food intake). The combination of these costs and benefits determine what optimal behavior is. In the present study, we employ a foraging-style task to study how humans make reward-based choices in response to the real-time constraints of a dynamic environment. On each trial participants were presented with two rewards that differed in magnitude and in the delay until their receipt. Because the experiment was of a fixed duration, maximizing earnings required decision makers to determine how to trade off the magnitude and the delay associated with the two rewards on each trial. To evaluate the extent to which participants could adapt to the decision environment, specific task characteristics were manipulated, including reward magnitudes (Experiment 1) and the delay between trials (Experiment 2). Each of these manipulations was designed to alter the pattern of choices made by an optimal decision maker. Several findings are of note. First, different choice strategies were observed with the manipulated environmental constraints. Second, despite contextually-appropriate shifts in behavior between conditions in each experiment, choice patterns deviated from theoretical optimality. In particular, the delays associated with the rewards did not exert a consistent influence on choices as required by exponential discounting. Third, decision makers nevertheless performed surprisingly well in all task environments with any deviations from strict optimality not having particularly deleterious effects on earnings. Taken together, these results suggest that human decision makers are capable of exhibiting intertemporal preferences that reflect a variety of environmental constraints.

Recent theoretical models suggest that deciding about actions and executing them are not implemented by completely distinct neural mechanisms but are instead two modes of an integrated dynamical system. Here, we investigate this proposal by examining how neural activity unfolds during a dynamic decision-making task within the high-dimensional space defined by the activity of cells in monkey dorsal premotor (PMd), primary motor (M1), and dorsolateral prefrontal cortex (dlPFC) as well as the external and internal segments of the globus pallidus (GPe, GPi). Dimensionality reduction shows that the four strongest components of neural activity are functionally interpretable, reflecting a state transition between deliberation and commitment, the transformation of sensory evidence into a choice, and the baseline and slope of the rising urgency to decide. Analysis of the contribution of each population to these components shows meaningful differences between regions but no distinct clusters within each region, consistent with an integrated dynamical system. During deliberation, cortical activity unfolds on a two-dimensional “decision manifold” defined by sensory evidence and urgency and falls off this manifold at the moment of commitment into a choice-dependent trajectory leading to movement initiation. The structure of the manifold varies between regions: In PMd, it is curved; in M1, it is nearly perfectly flat; and in dlPFC, it is almost entirely confined to the sensory evidence dimension. In contrast, pallidal activity during deliberation is primarily defined by urgency. We suggest that these findings reveal the distinct functional contributions of different brain regions to an integrated dynamical system governing action selection and execution.

PurposeIntroducing additive manufacturing (AM) in a multinational corporation with a global spare parts operation requires tools for a dynamic supplier selection, considering both cost and delivery performance. In the switchover to AM from conventional manufacturing, the objective of this study is to find situations and ways to improve the spare parts service to end customers.Design/methodology/approachIn this explorative study, the authors develop a procedure – in collaboration with the spare parts operations managers of a case company – for dynamic operational decision-making for the selection of spare parts supply from multiple suppliers. The authors' design proposition is based on a field experiment for the procurement and delivery of 36 problematic spare parts.FindingsThe practice intervention verified the intended outcomes of increased cost and delivery performance, yielding improved customer service through a switchover to AM according to situational context. The successful operational integration of dynamic additive and static conventional supply was triggered by the generative mechanisms of highly interactive model-based supplier relationships and insignificant transaction costs.Originality/valueThe dynamic decision-making proposal extends the product-specific make-to-order practice to the general-purpose build-to-model that selects the mode of supply and supplier for individual spare parts at an operational level through model-based interactions with AM suppliers. The successful outcome of the experiment prompted the case company to begin the introduction of AM into the company's spare parts supply chain.

Unmanned aerial vehicles, commonly referred to as drones , have recently seen an increased level of interest as their potential use in same-day home delivery has been promoted and advocated by large retailers and courier delivery companies. We introduce a novel way to exploit drones in same-day home delivery settings: drone resupply. We consider a home delivery system in which delivery trucks are regularly resupplied by drones. Resupply can take place whenever a delivery truck is stationary and a drone can land on the truck’s roof. We introduce the vehicle routing problem with drone resupply to capture and investigate this setting. We develop different algorithms and compare their performance. Finally, we quantify the potential benefits of drone resupply and generate valuable insights for advancing this concept.

This paper presents a dynamic discrete choice model (DDCM) for daily activity–travel planning. A daily activity–travel pattern is constructed from a sequence of decisions of when, where, why, and how to travel. Individuals’ preferences for activity–travel patterns are described by the sum of the utility of all travel and activity episodes in that pattern, but components of the utility functions, such as travel times, may be stochastic. In each decision stage, individuals act as if they maximized the expected utility of the remainder of the day. The DDCM-model presented allows for a detailed treatment of timing decision consistent with other choice dimensions, respects time–space constraints, and enables the inclusion of explicitly modeled uncertainties in, for example, travel time. In a case study, a model for daily planning of activity and travel on workdays is estimated whereby individuals can perform any number of trips that each is a combination of one of 1,240 locations, four modes, and six activities. Simulation results indicate that the model within sample accurately replicates timing decisions, trip lengths, and the distributions of the number of trips, tours, and trips per tour.

We investigate how multiple actors accomplish interdependent routine performances directed at novel intended outcomes and how this affects routine dynamics over time. We report findings from a longitudinal ethnographic study in an automotive company where actors developed a new business model around information-based services. By analyzing episodes involving interdependent routines, we develop a process model of routine work and dynamics across routines. We identify three types of routine work (flexing, stretching, and inventing) that generate increasingly novel actions and outcomes. Flexed, stretched, and invented performances create emerging consequences for further actions across routines and surface differences between actors that could lead to breakdowns of routine work. Actors respond to such consequences through iterative and cascading episodes of routine work. We discuss how our findings provide new insights in efforts to create variable routine performances and the consequences of interdependence for routine dynamics.

This paper examines how routine patterns are recognized as either stable or flexible and which mechanisms are enacted to maintain this patterning work. We address this question through an ethnographic case study analyzing how a catastrophe management organization enacts routines in a highly dynamic setting. Our findings first of all reveal that patterns described by the participants as either stable or flexible were nevertheless both performed differently in each iteration of the routine. Our microlevel analysis shows that to enact patterns that participants perceive as stable, participants had to carry out specific aligning and prioritizing activities that lock-stepped performances. In contrast, participants perceive patterns as flexible when they enact specific selecting and recombining activities. Building on these observations, we add to extant routine literature by (1) differentiating between stability, standardization, flexibility, and change of routines and by (2) providing new insights on mindfulness in accounting for the microlevel activities enacted to orient toward a pattern that enhances standardization or flexibility in dynamic contexts. Moreover, (3) our insights point to the centrality of knowing for the enactment and recognition of patterning work.

This paper presents a multi-source data-driven hybrid path planning framework that integrates global A* search with local Deep Q-Network (DQN) optimization to address complex terrestrial routing challenges. By fusing ASTER GDEM terrain data with OpenStreetMap (OSM) road networks, we construct a standardized geospatial database encompassing elevation, traffic, and road attributes. A dynamic-heuristic A* algorithm is proposed, incorporating traffic signals and congestion penalties, and is enhanced by a DQN-based local decision module to improve adaptability to dynamic environments. Experimental results on a realistic urban dataset demonstrate that the proposed method achieves superior performance in risk avoidance, travel time reduction, and dynamic obstacle handling compared to traditional models. This study contributes a unified architecture that enhances planning robustness and lays the foundation for real-time applications in emergency response and smart logistics.