A simulation approach to decision making in IT service strategy.
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ABSTRACT:
SUBMITTER: Orta E
PROVIDER: S-EPMC3984762 | biostudies-other | 2014
REPOSITORIES: biostudies-other
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Project description:Decision-making is a high-level cognitive process based on cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of cognitive modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar-based and a rule-based approach which incorporates perceptual-motor and metacognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the modeling data. However, we found a larger variance and a lower overall performance of the modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional information about the underlying cognitive processes from behavioral, psychobiological and neurophysiological data may help to optimize future applications of this model such that it can be transferred to other domains of comparable dynamic decision tasks.
| S-EPMC5543095 | biostudies-other
Project description:BackgroundCritical limb ischemia (CLI) is a feared complication of peripheral vascular disease that often requires surgical management and may require amputation of the affected limb. We developed a decision model to inform clinical management for a 63-year-old woman with CLI and multiple medical comorbidities, including advanced heart failure and diabetes.MethodsWe developed a Markov decision model to evaluate 4 strategies: amputation, surgical bypass, endovascular therapy (e.g. stent or revascularization), and medical management. We measured the impact of parameter uncertainty using 1-way, 2-way, and multiway sensitivity analyses.ResultsIn the base case, endovascular therapy yielded similar discounted quality-adjusted life months (26.50 QALMs) compared with surgical bypass (26.34 QALMs). Both endovascular and surgical therapies were superior to amputation (18.83 QALMs) and medical management (11.08 QALMs). This finding was robust to a wide range of periprocedural mortality weights and was most sensitive to long-term mortality associated with endovascular and surgical therapies. Utility weights were not stratified by patient comorbidities; nonetheless, our conclusion was robust to a range of utility weight values.ConclusionsFor a patient with CLI, endovascular therapy and surgical bypass provided comparable clinical outcomes. However, this finding was sensitive to long-term mortality rates associated with each procedure. Both endovascular and surgical therapies were superior to amputation or medical management in a range of scenarios.
| S-EPMC5643229 | biostudies-literature
Project description:Animal approach-avoidance conflict paradigms have been used extensively to operationalize anxiety, quantify the effects of anxiolytic agents, and probe the neural basis of fear and anxiety. Results from human neuroimaging studies support that a frontal-striatal-amygdala neural circuitry is important for approach-avoidance learning. However, the neural basis of decision-making is much less clear in this context. Thus, we combined a recently developed human approach-avoidance paradigm with functional magnetic resonance imaging (fMRI) to identify neural substrates underlying approach-avoidance conflict decision-making. Fifteen healthy adults completed the approach-avoidance conflict (AAC) paradigm during fMRI. Analyses of variance were used to compare conflict to nonconflict (avoid-threat and approach-reward) conditions and to compare level of reward points offered during the decision phase. Trial-by-trial amplitude modulation analyses were used to delineate brain areas underlying decision-making in the context of approach/avoidance behavior. Conflict trials as compared to the nonconflict trials elicited greater activation within bilateral anterior cingulate cortex, anterior insula, and caudate, as well as right dorsolateral prefrontal cortex (PFC). Right caudate and lateral PFC activation was modulated by level of reward offered. Individuals who showed greater caudate activation exhibited less approach behavior. On a trial-by-trial basis, greater right lateral PFC activation related to less approach behavior. Taken together, results suggest that the degree of activation within prefrontal-striatal-insula circuitry determines the degree of approach versus avoidance decision-making. Moreover, the degree of caudate and lateral PFC activation related to individual differences in approach-avoidance decision-making. Therefore, the approach-avoidance conflict paradigm is ideally suited to probe anxiety-related processing differences during approach-avoidance decision-making.
| S-EPMC4300249 | biostudies-literature
Project description:This study aims to explore how physicians make sense of and give meaning to their decision-making during obstetric emergencies. Childbirth is considered safe in the wealthiest parts of the world. However, variations in both intervention rates and delivery outcomes have been found between countries and between maternity units of the same country. Interventions can prevent neonatal and maternal morbidity but may cause avoidable harm if performed without medical indication. To gain insight into the possible causes of this variation, we turned to first-person perspectives, and particularly physicians' as they hold a central role in the obstetric team. This study was conducted at four maternity units in the southern region of Sweden. Using a narrative approach, individual in-depth interviews ignited by retelling an event and supported by art images, were performed between Oct. 2018 and Feb. 2020. In total 17 obstetricians and gynecologists participated. An inductive thematic narrative analysis was used for interpreting the data. Eight themes were constructed: (a) feeling lonely, (b) awareness of time, (c) sense of responsibility, (d) keeping calm, (e) work experience, (f) attending midwife, (g) mind-set and setting, and (h) hedging. Three decision-making perspectives were constructed: (I) individual-centered strategy, (II) dialogue-distributed process, and (III) chaotic flow-orientation. This study shows how various psychological and organizational conditions synergize with physicians during decision-making. It also indicates how physicians gave decision-making meaning through individual motivations and rationales, expressed as a perspective. Finally, the study also suggests that decision-making evolves with experience, and over time. The findings have significance for teamwork, team training, patient safety and for education of trainees.
| S-EPMC8791468 | biostudies-literature
Project description:Inferring parameters of computational models that capture experimental data are a central task in cognitive neuroscience. Bayesian statistical inference methods usually require the ability to evaluate the likelihood of the model-however, for many models of interest in cognitive neuroscience, the associated likelihoods cannot be computed efficiently. Simulation-based inference (SBI) offers a solution to this problem by only requiring access to simulations produced by the model. Previously, Fengler et al. introduced likelihood approximation networks (LANs, Fengler et al., 2021) which make it possible to apply SBI to models of decision-making, but require billions of simulations for training. Here, we provide a new SBI method that is substantially more simulation efficient. Our approach, mixed neural likelihood estimation (MNLE), trains neural density estimators on model simulations to emulate the simulator, and is designed to capture both the continuous (e.g., reaction times) and discrete (choices) data of decision-making models. The likelihoods of the emulator can then be used to perform Bayesian parameter inference on experimental data using standard approximate inference methods like Markov Chain Monte Carlo sampling. We demonstrate MNLE on two variants of the drift-diffusion model and show that it is substantially more efficient than LANs: MNLE achieves similar likelihood accuracy with six orders of magnitude fewer training simulations, and is significantly more accurate than LANs when both are trained with the same budget. Our approach enables researchers to perform SBI on custom-tailored models of decision-making, leading to fast iteration of model design for scientific discovery.
| S-EPMC9374439 | biostudies-literature
Project description:BackgroundFraming is known to influence decision making.ObjectiveThe study objective was to describe language used by physicians when discussing treatment options with a critically and terminally ill elder.MethodsHigh-fidelity simulation was used, involving an elder with end-stage cancer and life-threatening hypoxia, followed by a debriefing interview. Subjects were hospitalist, emergency medicine, and critical care physicians from three academic medical centers. Measures were observation of encounters in real time followed by content analysis of simulation and debriefing interview transcripts. During the simulation we identified the first mention ("broaching") of principal treatment options--intubation and mechanical ventilation (life-sustaining treatment [LST]) and palliation in anticipation of death (palliation)--and used constant comparative methods to identify language used. We identified physician opinions about the use of LST in this clinical context during the debriefing interviews, and compared language used with opinions.ResultsAmong 114 physician subjects, 106 discussed LST, 86 discussed palliation, and 84 discussed both. We identified five frames: will (decided), must (necessary), should (convention), could (option), and ask (elicitation of preferences). Physicians broached LST differently than palliation (p<0.01), most commonly framing LST as necessary (53%), while framing palliation as optional (49%). Among physicians who framed LST as imperative (will or must), 16 (30%) felt intubation would be inappropriate in this clinical situation.ConclusionsIn this high-fidelity simulation experiment involving a critically and terminally ill elder, the majority of physicians framed the available options in ways implying LST was the expected or preferred choice. Framing of treatment options could influence ultimate treatment decisions.
| S-EPMC4696426 | biostudies-literature
Project description:BACKGROUND:Migrant birds travel between their breeding areas and wintering grounds by alternating energetically and physiologically demanding flights with periods of rest and fuelling, so-called stopovers. An important intrinsic factor influencing the decision to resume migration is the amount of energy stores available for the next flight. Correlative studies with free-flying birds and experimental studies with caged birds have shown that the amount of energy stores affects the day-to-day, within-day and the directional decision of departure. The methodological advantages of both the correlative and experimental approach are combined when radio-tagging many individuals on the same day and subsequently determining the departure decisions at a high spatiotemporal resolution. Making use of such a quasi-experimental approach with an automated radio-tracking system at stopover, we studied the effect of energy stores on departure decisions and whether they vary between species of different migration strategies experiencing contrasting time constraints. For this, we chose a long-distance migrant, the common redstart (Phoenicurus phoenicurus), and a medium-distance migrant, the European robin (Erithacus rubecula), because the former has to travel at relatively higher speed to reach its wintering ground in a reasonable time at the expense of relatively higher energetic costs for travelling than the latter. RESULTS:Common redstarts with higher energy stores were more likely to resume migration than their conspecifics with lower energy stores, whereas this pattern was absent in the European robins. The amount of energy stores significantly affected the timing of departure within the day, with large energy stores yielding early departures in both species. Departure directions from the stopover site during the first night after capture were oriented towards the seasonally appropriate direction but were not affected by variation in energy stores. CONCLUSIONS:We demonstrate the importance of variation in energy stores on the departure decisions and that it may affect species with different migration strategies dissimilarly in autumn. Nevertheless, knowledge of other intrinsic factors, such as feeding conditions, health status and physiological consequences of previous flights, is additionally required to better understand the departure decisions of migrants, as this is the key to providing an overall assessment of the decision-making process.
| S-EPMC7346510 | biostudies-literature
Project description:The decisions we make are sometimes influenced by interactions with other agents. Previous studies have suggested that the prefrontal cortex plays an important role in decision-making and that the dopamine system underlies processes of motivation, motor preparation, and reinforcement learning. However, the physiological mechanisms underlying how the prefrontal cortex and the dopaminergic system are involved in decision-making remain largely unclear. The present study aimed to determine how decision strategies influence event-related potentials (ERPs). We also tested the effect of levodopa, a dopamine precursor, on decision-making and ERPs in a randomized double-blind placebo-controlled investigation. The subjects performed a matching-pennies task against an opposing virtual computer player by choosing between right and left targets while their ERPs were recorded. According to the rules of the matching-pennies task, the subject won the trial when they chose the same side as the opponent, and lost otherwise. We set three different task rules: (1) with the alternation (ALT) rule, the computer opponent made alternating choices of right and left in sequential trials; (2) with the random (RAND) rule, the opponent randomly chose between right and left; and (3) with the GAME rule, the opponent analyzed the subject's past choices to predict the subject's next choice, and then chose the opposite side. A sustained medial ERP became more negative toward the time of the subject's target choice. A biphasic potential appeared when the opponent's choice was revealed after the subject's response. The ERPs around the subject's choice were greater in RAND and GAME than in ALT, and the negative peak was enhanced by levodopa. In addition to these medial ERPs, we observed lateral frontal ERPs tuned to the choice direction. The signals emerged around the choice period selectively in RAND and GAME when levodopa was administered. These results suggest that decision processes are modulated by the dopamine system when a complex and strategic decision is required, which may reflect decision updating with dopaminergic prediction error signals.
| S-EPMC8017162 | biostudies-literature
Project description:When facing situations that involve risk and reward, some may focus on the opportunity for reward, whereas others may focus on potential risks. Here, we used an original set of pictorial scenarios to try and predict 3- to 8-year-olds' reward-seeking and risk-avoiding behavior in three decision-making scenarios (N = 99; Mage = 5.6; 47% girls). We found that children's reward-risk tendencies did not predict sharing behavior in a dictator-game 'sharing' task. However, they predicted children's monopolizing behavior in a dictator-game 'taking' task and their preferences between taking home a 'risky' or a 'safe' reward in a novel prize-preference task. Overall, using a set of original pictorial scenarios to assess individual differences early on in development now provides initial evidence that bridges individual differences and decision-making domains and exposes behavioral patterns that were thus far hidden.
| S-EPMC10365306 | biostudies-literature
Project description:BackgroundGood decision making has been increasingly cited as a core component of good medical care, and shared decision making is one means of achieving high decision quality. If it is to be a standard, good measures and protocols are needed for assessing the quality of decisions. Consistency with patient goals and concerns is one defining characteristic of a good decision. A new method for evaluating decision quality for major surgical decisions was examined, and a methodology for collecting the needed data was developed.MethodsFor a national probability sample of fee-for-service Medicare beneficiaries who had a coronary artery bypass graft (CABG), a lumpectomy or a mastectomy for breast cancer, or surgery for prostate cancer during the last half of 2008, a mail-survey of selected patients was carried out about one year after the procedures. Patients' goals and concerns, knowledge, key aspects of interactions with clinicians, and feelings about the decisions were assessed. A decision dissonance score was created that measured the extent to which patient ratings of goals ran counter to the treatment received. The construct and predictive validity of the decision dissonance score was then assessed.ResultsWhen data were averaged across all four procedures, patients with more knowledge and those who reported more involvement reported significantly lower Decision Dissonance Scores. Patients with lower Decision Dissonance Scores also reported more confidence in their decisions and feeling more positively about how the treatment turned out, and they were more likely to say that they would make the same decision again.ConclusionsSurveying discharged surgery patients is a feasible way to evaluate decision making, and Decision Dissonance appears to be a promising approach to validly measuring decision quality.
| S-EPMC3615253 | biostudies-literature