Individual Decision Strategies Research Articles

Feedback-related brain activity in individual decision: evidence from a gambling EEG study.

In this study, based on scalp electroencephalogram (EEG), we conducted cortical source localization and functional network analyses to investigate the underlying mechanism explaining the decision processes when individuals anticipate maximizing gambling benefits, particularly in situations where the decision outcomes are inconsistent with the profit goals. The findings shed light on the feedback monitoring process, wherein incongruity between outcomes and gambling goals triggers a more pronounced medial frontal negativity and activates the frontal lobe. Moreover, long-range theta connectivity is implicated in processing surprise and uncertainty caused by inconsistent feedback conditions, while middle-range delta coupling reflects a more intricate evaluation of feedback outcomes, which subsequently modifies individual decision-making for optimizing future rewards. Collectively, these findings deepen our comprehension of decision-making under circumstances where the profit goals are compromised by decision outcomes and provide electrophysiological evidence supporting adaptive adjustments in individual decision strategies to achieve maximum benefit.

StratEst: a software package for strategy frequency estimation

AbstractstratEst is a software package for strategy frequency estimation in the freely available statistical computing environment R (R Development Core Team in R Foundation for Statistical Computing, Vienna, 2022). The package aims at minimizing the start-up costs of running the modern strategy frequency estimation techniques used in experimental economics. Strategy frequency estimation (Stahl and Wilson in J Econ Behav Organ 25: 309–327, 1994; Stahl and Wilson in Games Econ Behav, 10: 218–254, 1995) models the choices of participants in an economic experiment as a finite-mixture of individual decision strategies. The parameters of the model describe the associated behavior of each strategy and its frequency in the data. stratEst provides a convenient and flexible framework for strategy frequency estimation, allowing the user to customize, store and reuse sets of candidate strategies. The package includes useful functions for data processing and simulation, strategy programming, model estimation, parameter testing, model checking, and model selection.

Agent-Based Model of a Blockchain Enabled Peer-to-Peer Energy Market: Application for a Neighborhood Trial in Perth, Australia

The transfer of market power in electric generation from utilities to end-users spurred by the diffusion of distributed energy resources necessitates a new system of settlement in the electricity business that can better manage generation assets at the grid-edge. A new concept in facilitating distributed generation is peer-to-peer energy trading, where households exchange excess power with neighbors at a price they set themselves. However, little is known about the effects of peer-to-peer energy trading on the sociotechnical dynamics of electric power systems. Further, given the novelty of the concept, there are knowledge gaps regarding the impact of alternative electricity market structures and individual decision strategies on neighborhood exchanges and market outcomes. This study develops an empirical agent-based modeling (ABM) framework to simulate peer-to-peer electricity trades in a decentralized residential energy market. The framework is applied for a case study in Perth, Western Australia, where a blockchain-enabled energy trading platform was trialed among 18 households, which acted as prosumers or consumers. The ABM is applied for a set of alternative electricity market structures. Results assess the impact of solar generation forecasting approaches, battery energy storage, and ratio of prosumers to consumers on the dynamics of peer-to-peer energy trading systems. Designing an efficient, equitable, and sustainable future energy system hinges on the recognition of trade-offs on and across, social, technological, economic, and environmental levels. Results demonstrate that the ABM can be applied to manage emerging uncertainties by facilitating the testing and development of management strategies.

Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning.

Successful human behaviour depends on the brain's ability to extract meaningful structure from information streams and make predictions about future events. Individuals can differ markedly in the decision strategies they use to learn the environment's statistics, yet we have little idea why. Here, we investigate whether the brain networks involved in learning temporal sequences without explicit reward differ depending on the decision strategy that individuals adopt. We demonstrate that individuals alter their decision strategy in response to changes in temporal statistics and engage dissociable circuits: extracting the exact sequence statistics relates to plasticity in motor corticostriatal circuits, while selecting the most probable outcomes relates to plasticity in visual, motivational and executive corticostriatal circuits. Combining graph metrics of functional and structural connectivity, we provide evidence that learning-dependent changes in these circuits predict individual decision strategy. Our findings propose brain plasticity mechanisms that mediate individual ability for interpreting the structure of variable environments.

Demand Control Management in Microgrids: The Impact of Different Policies and Communication Network Topologies

This paper studies how the communication network between proactive consumers affects the power utilization and fairness in a simplified direct-current microgrid model, composed of three coupled layers: physical (an electric circuit that represents a microgrid), communication (a peer-to-peer network within the microgrid), and regulatory (individual decision strategies). Our results show that, for optimal power utilization and fairness, a global knowledge about the system is needed, demonstrating the importance of a microgrid aggregator to inform about the power consumption for different time periods.

Learning predictive statistics from temporal sequences: Dynamics and strategies.

Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics—that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments.

Learning Predictive Statistics: Strategies and Brain Mechanisms.

When immersed in a new environment, we are challenged to decipher initially incomprehensible streams of sensory information. However, quite rapidly, the brain finds structure and meaning in these incoming signals, helping us to predict and prepare ourselves for future actions. This skill relies on extracting the statistics of event streams in the environment that contain regularities of variable complexity from simple repetitive patterns to complex probabilistic combinations. Here, we test the brain mechanisms that mediate our ability to adapt to the environment's statistics and predict upcoming events. By combining behavioral training and multisession fMRI in human participants (male and female), we track the corticostriatal mechanisms that mediate learning of temporal sequences as they change in structure complexity. We show that learning of predictive structures relates to individual decision strategy; that is, selecting the most probable outcome in a given context (maximizing) versus matching the exact sequence statistics. These strategies engage distinct human brain regions: maximizing engages dorsolateral prefrontal, cingulate, sensory–motor regions, and basal ganglia (dorsal caudate, putamen), whereas matching engages occipitotemporal regions (including the hippocampus) and basal ganglia (ventral caudate). Our findings provide evidence for distinct corticostriatal mechanisms that facilitate our ability to extract behaviorally relevant statistics to make predictions.SIGNIFICANCE STATEMENT Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. Past work has studied how humans identify repetitive patterns and associative pairings. However, the natural environment contains regularities that vary in complexity from simple repetition to complex probabilistic combinations. Here, we combine behavior and multisession fMRI to track the brain mechanisms that mediate our ability to adapt to changes in the environment's statistics. We provide evidence for an alternate route for learning complex temporal statistics: extracting the most probable outcome in a given context is implemented by interactions between executive and motor corticostriatal mechanisms compared with visual corticostriatal circuits (including hippocampal cortex) that support learning of the exact temporal statistics.

Choosing an Advanced Therapy inParkinson's Disease; isitanEvidence-Based Decision inCurrentPractice?

In advanced Parkinson's disease (PD), neurologists and patients face a complex decision for an advanced therapy. When choosing a treatment, the best available evidence should be combined with the professional's expertise and the patient's preferences. The objective of this study was to explore current decision-making in advanced PD. We conducted focus group discussions and individual interviews with patients (N = 20) who had received deep brain stimulation, Levodopa-Carbidopa intestinal gel, or subcutaneous apomorphine infusion, and with their caregivers (N = 16). Furthermore, we conducted semi-structured interviews with neurologists (N = 7) and PD nurse specialists (N = 3) to include the perspectives of all key players in this decision-making process. Data were analyzed by two researchers using a qualitative thematic analysis approach. Four themes representing current experiences with the decision-making process were identified: 1) information and information needs, 2) factors influencing treatment choice and individual decision strategies, 3) decision-making roles, and 4) barriers and facilitators to shared decision-making (SDM). Patient preferences were taken into account, however patients were not always provided with adequate information. The professional's expertise influenced the decision-making process in both positive and negative ways. Although professionals and patients considered SDM essential for the decision of an advanced treatment, they mentioned several barriers for the implementation in current practice. In this study we found several factors explaining why in current practice, evidence-based decision-making in advanced PD is not optimal. An important first step would be to develop objective information on all treatment options.

Choosing sides: The genetics of why we go with the loudest

Recent developments in spatial voting have moved beyond finding the most appropriate utility function and started to assess individual differences in decision strategy. The question is not if a proximity or directional worldview performs better in general, rather under what conditions do people pick one strategy over the other? We draw on psychological theories to develop a survey-based measure of individual decision strategy and take a behavior genetic route to explaining the individual differences. We argue that dispositional traits shape whether an individual develops a directional or proximity worldview of the political arena. Utilizing a classical twin design, we capitalize on the documented relationship between partisanship and a directionalist worldview. We find that, in the Minnesota Twin Political Survey, both the strength of party identification and directional voting are moderately (~20 percent) but significantly ( p < 0.05) heritable with no socialized component contributing to the variance. The covariation between the two traits is predominantly driven by common underlying genetic effects ( p < 0.01). Implications for the rational voter models are discussed in light of the findings.

Diagnostic Task Selection for Strategy Classification in Judgment and Decision Making: Theory, Validation, and Implementation in R

AbstractOne major statistical and methodological challenge in Judgment and Decision Making research is the reliable identification of individual decision strategies by selection of diagnostic tasks, that is, tasks for which predictions of the strategies differ sufficiently. The more strategies are considered, and the larger the number of dependent measures simultaneously taken into account in strategy classification (e.g., choices, decision time, confidence ratings; Glöckner, 2009), the more complex the selection of the most diagnostic tasks becomes. We suggest the Euclidian Diagnostic Task Selection (EDTS) method as a standardized solution for the problem. According to EDTS, experimental tasks are selected that maximize the average difference between strategy predictions for any multidimensional prediction space. In a comprehensive model recovery simulation, we evaluate and quantify the influence of diagnostic task selection on identification rates in strategy classification. Strategy classification with EDTS shows superior performance in comparison to less diagnostic task selection algorithms such as representative sampling. The advantage of EDTS is particularly large if only few dependent measures are considered. We also provide an easy-to-use function in the free software package R that allows generating predictions for the most commonly considered strategies for a specified set of tasks and evaluating the diagnosticity of those tasks via EDTS; thus, to apply EDTS, no prior programming knowledge is necessary.

Themes affecting health-care consumers’ choice of a hospital for elective surgery when receiving web-based comparative consumer information

Objective To get insights into the decision-making strategy of health-care consumers when confronted with comparative consumer information. Methods Qualitative descriptive study among 18 consumers who had a hip or knee replacement no longer than five years ago. To study their decision-making strategies a paper draft for a website was used providing comparative consumer information. Data were collected by cognitive interviews and focus-group meetings and subjected to thematic analysis. Results Consumers were able to understand the presented information, but had problems to use it as a decision aid. They primarily relied on previous experiences. Four themes were revealed: decision making, perceived benefits, unmet information needs, and trustworthiness. Consumers used different decision strategies and showed unpredictable behavior when choosing a hospital. Conclusion Individual decision strategies, unsatisfied information needs, limited tenability and too coarse aggregation levels of quality scores are barriers for a proper use of comparative consumer information. Personal experience remains a valuable information source for hospital selection. We suggest that a website presenting comparative consumer information should be flexible in various ways and should include functionality to share personal experience.

Assessment of actions in a multi-actor and multicriteria framework: application to the refunding of microfinance institutions

This article formulates a methodology that describes the decision making process in which a group of actors (in this case, microfinance experts) become involved in actions. The actions, Microfinance Institutions (MFIs) being selected for refinancing, are described using several conflictual, ordinal criteria. This methodology is constructed in three consecutive steps: the first involves obtaining decision strategies from interactive input received from experts, the second compiles and merges individual decision strategies so as to create a common decision base and the third involves using this common decision base to construct a decision-making aid destined to help experts with the decision-making process. This methodology was tested with five microfinance experts in Cameroon.

Internet Multiattribute Group Decision Support in Electronic Commerce

The rapid growth of the Internet has provided the means for distributed organizational decision making for electronic commerce. Members of organizations can jointly investigate products, exchange information, and make decisions on-line from remote sites. Internet-based multiattribute group decision making is characterized by three aspects, (i) individual interactive decision making, (ii) communication means, and (iii) group consensus reaching. The purpose of this research was to study the role of communication and individual decision strategies and their influence on multiattribute group decision making and consensus reaching in organizational electronic commerce settings. The results of this study indicate that analytic decision support is indispensable in collaborative Internet-based decision making, that a perfect match of analytic decision support and communication channels must be achieved, and that efficiency of individual decision support should be compromised for higher confidence in the group's decisions. The results of this study also confirm findings by Haubl and Trifts (2000) that interactive decision analytic support has positive effects on the quality and efficiency of individual decision making, and findings by Limayem and DeSanctis (2000) and Todd and Benbesat (2000), that decision makers will use normative decision models if they require little effort and if decisional guidance is provided. The conclusion drawn from this study is that the continuously evolving Internet technology for collaborative decision making is only one aspect for better organizational decision making – the crucial aspect, however, will be the development and optimal integration of analytic decision models, communication channels, and consensus reaching mechanisms.

Organization of person memory and retrieval processes in recognition

AbstractThe main concern of the present study was the question whether behavioural information and traits could be accessed independently. In a modified recognition experiment person descriptions were presented; later, a behaviour or a trait was presented as test item and subjects had to decide whether the behaviour was included in the description or the trait could be inferred. The decision time for behaviours as test items turned out to be independent of the number of traits inferrable. This finding supports two‐memory conceptions of person memory according to which of two kinds of representations are available. One representation is trait‐based and in the other one behavioural information is available. This conclusion was confirmed by the result that the decision time for traits as test items is partially invariant with respect to the number of behaviours included in the person description. Distinct individual diferences in the relation between decision time for traits as test items and the number of behaviours appeared which imply individual decision strategies in recognizing persons.

Modeling Decisions to Use Tube Feeding in Seriously Ill Patients

Clinical decisions to use life-sustaining technologies, such as tube feeding in seriously ill patients, depend on many important factors. Using case simulations, we analyzed the decisions of students, housestaff, and faculty of Temple University School of Medicine, Philadelphia, to use tube feeding in seriously ill patients. Although patient preference was the most important factor for most respondents, we observed three different patterns of decision strategies. Those in the first group, the autonomists (34% of respondents), considered only patient preference; those in the second group, the mixed strategists (56% of respondents), included patient preference among other important factors; and those in the third group, the paternalists (10% of respondents), used factors other than patient preference. More experienced clinicians and those individuals who believed tube feeding represented an extraordinary level of care were significantly less likely to begin tube feeding in the case simulations. Description of these individual decision strategies and elucidation of the important physician characteristics can help identify the potential ethical dilemmas in these different clinical decisions.