Accelerating depression intervention: identifying critical psychological factors using MCDM-MOORA technique for early therapy initiation.

BACKGROUND: A thorough psychosocial assessment is time-consuming, often requiring multiple sessions to uncover the psychological factors contributing to mental illness, such as depression. The duration varies depending on the severity of the patient's condition and how effectively the psychotherapist can establish rapport. However, prolonged assessment periods pose a significant risk of patient deterioration. METHODS: The comprehensive psychosocial intervention, led by the Multi-Criteria Decision-Making (MCDM) approach utilizing the Multi-Objective Optimization by Ratio Analysis (MOORA) method, played a pivotal role in identifying the key psychological factors contributing to the depression of the client among the 21 factors specified by BDI-II analysis. RESULTS: The integration of the MOORA strategy compared to traditional psychotherapy on 254 samples demonstrates a Jaccard similarity coefficient of 0.8, with a minimum error margin of 7% (vulnerability index = 0.57), indicating a significant agreement between the two approaches, both converging towards a similar solution. For patients with extreme depression, the number of sessions reduced from 18 ± 2 to 11 ± 2, showing a 33-35% reduction (χ2 = 6.94, p = 0.008). Severe depression patients experienced a reduction from 14 ± 2 to 8 ± 1 sessions i.e., 34-39% reduction (χ2 = 8.32, p = 0.004). Moderate depression patients saw sessions drop from 9 ± 1 to 5 ± 1, i.e., 37-43% reduction (χ2 = 0.29, p = 0.001). The accuracy for detecting dominant psychological factors improved to 82.88% for extreme, 86.74% for severe, and 90.34% for moderate depression, respectively. CONCLUSION: The implementation of MOORA facilitated the identification and prioritization of key psychosocial intervention strategies, making the process significantly faster compared to traditional methods. This acceleration greatly enhanced the precision and efficacy of the work. Additionally, critical vulnerable factors were identified through ordered statistics and correlation analysis [Pearson (r) = 0.8929 and Spearman's rank (ρ) = 0.7551] on the Beck Depression Inventory-II model. These findings were supported by other MCDM schemes such as EDAS and TOPSIS, demonstrating high stability and robustness in dynamic decision-making environments, maintaining consistency across scenarios adapted by different psychotherapists. Overall, the combined application of MCDM (MOORA) and targeted psychological interventions yielded substantial positive outcomes in enhancing the well-being of individuals with psychological illnesses, such as depression, cognitive, affective, and somatic syndromes.

q-Rung orthopair fuzzy dynamic aggregation operators with time sequence preference for dynamic decision-making.

The q-rung orthopair fuzzy set (q-ROPFS) is a kind of fuzzy framework that is capable of introducing significantly more fuzzy information than other fuzzy frameworks. The concept of combining information and aggregating it plays a significant part in the multi-criteria decision-making method. However, this new branch has recently attracted scholars from several domains. The goal of this study is to introduce some dynamic q-rung orthopair fuzzy aggregation operators (AOs) for solving multi-period decision-making issues in which all decision information is given by decision makers in the form of "q-rung orthopair fuzzy numbers" (q-ROPFNs) spanning diverse time periods. Einstein AOs are used to provide seamless information fusion, taking this advantage we proposed two new AOs namely, "dynamic q-rung orthopair fuzzy Einstein weighted averaging (DQROPFEWA) operator and dynamic q-rung orthopair fuzzy Einstein weighted geometric (DQROPFEWG) operator". Several attractive features of these AOs are addressed in depth. Additionally, we develop a method for addressing multi-period decision-making problems by using ideal solutions. To demonstrate the suggested approach's use, a numerical example is provided for calculating the impact of "coronavirus disease" 2019 (COVID-19) on everyday living. Finally, a comparison of the proposed and existing studies is performed to establish the efficacy of the proposed method. The given AOs and decision-making technique have broad use in real-world multi-stage decision analysis and dynamic decision analysis.

Minimap: An interactive dynamic decision making game for search and rescue missions.

Many aspects of humans' dynamic decision-making (DDM) behaviors have been studied with computer-simulated games called microworlds. However, most microworlds only emphasize specific elements of DDM and are inflexible in generating a variety of environments and experimental designs. Moreover, despite the ubiquity of gridworld games for Artificial Intelligence (AI) research, only some tools exist to aid in the development of browser-based gridworld environments for studying the dynamics of human decision-making behavior. To address these issues, we introduce Minimap, a dynamic interactive game to examine DDM in search and rescue missions, which incorporates all the essential characteristics of DDM and offers a wide range of flexibility regarding experimental setups and the creation of experimental scenarios. Minimap specifically allows customization of dynamics, complexity, opaqueness, and dynamic complexity when designing a DDM task. Minimap also enables researchers to visualize and replay recorded human trajectories for the analysis of human behavior. To demonstrate the utility of Minimap, we present a behavioral experiment that examines the impact of different degrees of structural complexity coupled with the opaqueness of the environment on human decision-making performance under time constraints. We discuss the potential applications of Minimap in improving productivity and transparent replications of human behavior and human-AI teaming research. We made Minimap an open-source tool, freely available at  https://github.com/DDM-Lab/MinimapInteractiveDDMGame .

Prediction, Explanation, and Control: The Use of Mental Models in Dynamic Environments.

The abilities to predict, explain, and control might arise out of operations on a common underlying representation or, conversely, from independent cognitive processes. We developed a novel experimental paradigm to explore how individuals might use probabilistic mental models in these three tasks, under varying levels of complexity and uncertainty. Participants interacted with a simple chatbot defined by a finite-state machine, and were then tested on their ability to predict, explain, and control the chatbot's responses. When full information was available, performance varied significantly across the tasks, with control proving most robust to increased complexity, and explanation being the most challenging. In the presence of hidden information, however, performance across tasks equalized, and participants demonstrated an alternative neglect bias, i.e., a tendency to ignore less likely possibilities. A second, within-subject experimental design then looked for correlations between abilities. We did not find strong correlations, but the challenges of the task for the subjects limited our statistical power. To understand these effects better, a final experiment investigated the possibility of cross-training, skill transfer, or "zero-shot" performance: how well a participant, explicitly trained on one of the three tasks, could perform on the others without additional training. Here we found strong asymmetries: participants trained to control gained generalizable abilities to both predict and explain, while training on either prediction or explanation did not lead to transfer. This cross-training experiment also revealed correlations in performance; most notably between control and prediction. Our findings highlight the complex role of mental models, in contrast to task-specific heuristics, when information is partially hidden, and suggest new avenues for research into situations where the acquisition of general purpose mental models may provide a unifying explanation for a variety of cognitive abilities.

Supporting detection of hostile intentions: automated assistance in a dynamic decision-making context.

In a dynamic decision-making task simulating basic ship movements, participants attempted, through a series of actions, to elicit and identify which one of six other ships was exhibiting either of two hostile behaviors. A high-performing, although imperfect, automated attention aid was introduced. It visually highlighted the ship categorized by an algorithm as the most likely to be hostile. Half of participants also received automation transparency in the form of a statement about why the hostile ship was highlighted. Results indicated that while the aid's advice was often complied with and hence led to higher accuracy with a shorter response time, detection was still suboptimal. Additionally, transparency had limited impacts on all aspects of performance. Implications for detection of hostile intentions and the challenges of supporting dynamic decision making are discussed.

A Cognitive Template for Management of Perioperative Adverse Events.

Perioperative crisis management commonly involves both rapid generic responses and slower abstract reasoning for the successful management of adverse events. A metacognitive approach to this process offers the potential for minimizing errors and thereby improving outcomes. One such metacognitive technique uses templates that guide dynamic decisionmaking. Because stressful circumstances impair memory and cognitive function, templates may be particularly useful during crises both to improve functional recall and to provide mental constructs that compel anesthesia providers to organize their thoughts and direct approaches to problem-solving that rely on critical thinking rather than solely on heuristics. A six-step cognitive template is proposed for formulating a working diagnosis and deciding appropriate therapy during a perioperative adverse event. The template utilizes overlapping differential diagnoses organized using principles of anatomy and/or physiology. It has been effective in nurse anesthesia training to promote a metacognitive approach to decisionmaking during such events, and the template can be widely utilized in nonacademic settings for similar purposes.

Choice adaptation to changing environments: trends, feedback, and observability of change.

Making successful decisions in dynamic environments requires that we adapt our actions to the changing environmental conditions. Past research has found that people are slow to adapt their choices when faced with change, they tend to be over-reliant on initial experiences, and they are susceptible to factors such as feedback and the direction of change (trend). We build on these findings using two experiments that manipulate feedback and trend in a binary choice task, where decisions are made from experience. Feedback was either partial (providing only the outcome of the selected choice) or full (providing outcomes of the selected and the forgone choice) and the expected value of one option either increased, decreased, or remained constant. Crucially, although the two choice options had equal expected value averaged across all trials, their expected values on individual trials differed, and halfway through 100 choice trials the choice option with higher expected value switched, requiring participants to adapt their choices in order to maximize their outcomes. In Experiment 1, the probability of receiving the high-value outcome changed over time. In Experiment 2, the outcome value changed over time. Generally, we found that participants had trouble adapting to change: full feedback led to more maximization than partial feedback before the switch but did not make a difference after the switch, suggesting stickiness and poor adaptation. Slightly better adaptation was found for changing outcome values over changing probabilities, implying that the observability of the element of change influences adaptation.

Patterns of choice adaptation in dynamic risky environments.

An important aspect of making good decisions is the ability to adapt to changes in the values of available choice options, and research suggests that we are poor at changing behavior and adapting our choices successfully. The current paper contributes to clarifying the role of memory on learning and successful adaptation to changing decision environments. We test two aspects of changing decision environments: the direction of change and the type of feedback. The direction of change refers to how options become more or less rewarding compared to other options, over time. Feedback refers to whether full or partial information about decision outcomes is received. Results from behavioral experiments revealed a robust effect of the direction of change: risk that becomes more rewarding over time is harder to detect than risk that becomes less rewarding over time; even with full feedback. We rely on three distinct computational models to interpret the role of memory on learning and adaptation. The distributions of individual model parameters were analyzed in relation to participants' ability to successfully adapt to the changing conditions of the various decision environments. Consistent across the three models and two distinct data sets, results revealed the importance of recency as an individual memory component for choice adaptation. Individuals relying more on recent experiences were more successful at adapting to change, regardless of its direction. We explain the value and limitations of these findings as well as opportunities for future research.

Learning when to wait and when to act.

Choosing how long to wait in order to optimize reward is a complex decision. We embedded these decisions within a video-game environment in which the amount of reward smoothly increased the longer one waited. The availability of external cues varied in order to determine how they affected the decision to wait to achieve the goal of maximizing the reward rate. As a group, people were most optimal when they could directly observe the growth in reward, and this information overshadowed a static color cue that did not require extended observation. These results were considered within the context of improving the choice between acting versus waiting in order to maximize reward rates.

"Oh no, the forest is burning!" cultural differences in the complex problem-solving process only under high uncertainty.

What do people in different cultures do when they encounter complex problems? Whereas some cross-cultural research exists about complex problem-solving predictors and performance, the process has rarely been studied. We presented participants from Brazil, Germany, the Philippines, and the United States with two computer-simulated dynamic problems, one where quick action was required - the WinFire simulation - and one where cautious action was required - the Coldstore simulation. Participants were asked to think aloud in their native language while working on these two tasks. These think-aloud protocols were digitally recorded, transcribed, and coded by coders in each country in terms of the steps involved in complex problem solving and dynamic decision making. For the current study, we developed a program to calculate transition frequencies from one problem solving step to another and analyzed only those protocols with more than 15 transitions. For WinFire, these were 256 think-aloud protocols from the four countries with a total of 12,542 statement, for Coldstore, these were 247 participants with a total of 15,237 statements. Based on previous, limited cross-cultural research, we predicted that after identifying a problem, Brazilians would make emotional and self-related statements, Germans would engage primarily in planning, Filipinos would gather additional information, and Americans would primarily state solutions. Results of latent transition analysis partially support these hypotheses, but only in the highly uncertain Coldstore situation and not in the more transparent WinFire situation. Transition frequencies were then also analyzed regarding community clusters using the spinglass algorithm in R, igraph. Results highlight the importance of process analyses in different tasks and show how cultural background guides people's decisions under uncertainty.

A Value-of-Information Framework for Personalizing the Timing of Surveillance Testing.

BACKGROUND: Patient surveillance using repeated biomarker measurements presents an opportunity to detect and treat disease progression early. Frequent surveillance testing using biomarkers is recommended and routinely conducted in several diseases, including cancer and diabetes. However, frequent testing involves tradeoffs. Although surveillance tests provide information about current disease status, the complications and costs of frequent tests may not be justified for patients who are at low risk of progression. Predictions based on patients' earlier biomarker values may be used to inform decision making; however, predictions are uncertain, leading to decision uncertainty. METHODS: We propose the Personalized Risk-Adaptive Surveillance (PRAISE) framework, a novel method for embedding predictions into a value-of-information (VOI) framework to account for the cost of uncertainty over time and determine the time point at which collection of biomarker data would be most valuable. The proposed sequential decision-making framework is innovative in that it leverages the patient's longitudinal history, considers individual benefits and harms, and allows for dynamic tailoring of surveillance intervals by considering the uncertainty in current information and estimating the probability that new information may change treatment decisions, as well as the impact of this change on patient outcomes. RESULTS: When applied to data from cystic fibrosis patients, PRAISE lowers costs by allowing some patients to skip a visit, compared to an "always test" strategy. It does so without compromising expected survival, by recommending less frequent testing among those who are unlikely to be treated at the skipped time point. CONCLUSIONS: A VOI-based approach to patient monitoring is feasible and could be applied to several diseases to develop more cost-effective and personalized strategies for ongoing patient care. HIGHLIGHTS: In many patient-monitoring settings, the complications and costs of frequent tests are not justified for patients who are at low risk of disease progression. Predictions based on patient history may be used to individualize the timing of patient visits based on evolving risk.We propose Personalized Risk-Adaptive Surveillance (PRAISE), a novel method for personalizing the timing of surveillance testing, where prediction modeling projects the disease trajectory and a value-of-information (VOI)-based pragmatic decision-theoretic framework quantifies patient- and time-specific benefit-harm tradeoffs.A VOI-based approach to patient monitoring could be applied to several diseases to develop more personalized and cost-effective strategies for ongoing patient care.

The effect of broadband noise on learning and dynamic decision-making and how cognitive workload and sex moderate its effect.

The aim of this research was to examine how broadband noise which is present in many workplaces affects dynamic decision-making. The effect of potential moderating factors, cognitive workload and sex, were also examined. Forty-eight participants (24 females) with an average age of 27.38 years (SD = 12.34) were asked to complete a dynamic decision-making task over three consecutive-days. Independent variables were Noise (Broadband - 0dBA vs. 75dBA above background) and Cognitive Workload (Low vs. High, manipulated via presence of a secondary task). Among females, broadband noise significantly impaired performance in low workload, but significantly improved performance in high workload. In contrast, among males broadband noise had no significant effect on overall performance. From an applied perspective, understanding the interaction between noise, cognitive workload and sex allows for the design of a training environment to ensure maximum performance by all staff.

Learning and Dynamic Decision Making.

Humans make decisions in dynamic environments (increasingly complex, highly uncertain, and changing situations) by searching for potential alternatives sequentially over time, to determine the best option at a precise moment. Surprisingly, the field of behavioral decision making has little to offer in terms of theoretical principles and practical guidelines on how people make decisions in dynamic situations. My research program aims to fill in this gap by developing theoretical understandings of decision processes as well as practical demonstrations of how these theoretical developments can improve human dynamic decision making. Throughout my research career, I have helped create, test, and improve a general theory of dynamic decision making, instance-based learning theory, IBLT. The methods I have used to contribute to IBLT are (1) laboratory experiments that rely on dynamic games in which humans make choices over time and space, individually and in teams, and from which we extrapolate robust phenomena and behavioral insights; and (2) computational, actionable cognitive models, which specify the decision-making process and the cognitive mechanisms involved into a computational algorithm. The combination of these methods spawned novel applications in areas such as cybersecurity, phishing, climate change, and human-machine interactions. In this paper, I will take you through my own intellectual exploratory experience of computational modeling of human decision processes, and how the integration of experimental work and cognitive modeling helped in discovering and uncovering the field of dynamic decision making.

Dynamic decision making and value computations in medial frontal cortex.

Dynamic decision making requires an intact medial frontal cortex. Recent work has combined theory and single-neuron measurements in frontal cortex to advance models of decision making. We review behavioral tasks that have been used to study dynamic decision making and algorithmic models of these tasks using reinforcement learning theory. We discuss studies linking neurophysiology and quantitative decision variables. We conclude with hypotheses about the role of other cortical and subcortical structures in dynamic decision making, including ascending neuromodulatory systems.

The Benefits and Costs of Cybersecurity Risk Reduction: A Dynamic Extension of the Gordon and Loeb Model.

This article develops a dynamic extension of the classic model of cybersecurity investment formulated by Gordon and Loeb. In this dynamic model, results are influenced by the rate at which cybersecurity assets depreciate and the rate of return on investment. Depreciation costs are lower in the dynamic model than is implicitly assumed in the classic model, while the rate-of-return threshold is higher. On balance, the user cost of cybersecurity assets is lower in the dynamic model than is implicitly assumed in the classic model. This difference increases the economically efficient size of the cybersecurity system in value terms, increasing the efficient level of risk reduction.

Evaluation of Decision-Making Chains and their Fractal Dimensions.

A decision-making process is a part of the decision-making theory, reasonably placing a major research interest on the question how the process is conducted and what affects the process itself in general. Naturally it is perceived as a sequence of steps, where things are moving forward little-by-little towards to the settled goal. An analysis could be done before (planning), during the process (control + adaption) or afterwards (analysis and evaluation). Also, we can just study someone's decision process first, mainly trying to avoid making "their" mistakes. Anyway, making decisions or just observing and studying them is a part of life. Either one assumes evaluation of the current situation and of the expected outcomes, assigning to each decision some "quality" according to the fixed set of criteria (like probabilistic), or the flexible ones (different heuristics). Thus, from the mathematical and the philosophic points of view we will face three principle questions applicable to any particular decision-making theory: (1) How many criteria do we need? (2) How well they are defined/described? (3) Are there any relations between them, or we can consider them to be independent ones? Besides, any admissible theory also will consider some kind of underground efficiency questions (at least not to over-complicate and postpone a decision-making process), possibility to track and secure the major and intermediate goals and et cetera. It is clear that theoretical research and even the hated ad-hoc hypothesis use some reasonable assumptions about criteria selection and their quantity: pure or context oriented, but we want to consider the presented problem without restrictions of any specific theory, domain or context; using just common sense and analogies between exact and human sciences detected in twentieth century an later. Therefore, we created a hypothesis on how many evaluation criteria do we really need to operate inside an abstract decision domain-regardless the nature of criteria and their relations with real-world processes. Actually, it was not a big surprise that it resulted to be related with concepts of fractals, chaos and the notion of the fractal dimension. Their clear presence was discovered in many social and biological sciences recently, so an investigation was continued not only in terms of finding "deep" arguments to prove our postulates: recent results in math and physics also showed that most dynamic processes could be described differently considering an analysis of the current situation, short-term and long-term runs. Hence, the nature and the quantity of the involved criteria may vary (they could be implicitly time-dependent) and we need to study this kind of relation also.

A deep learning solution to recommend laboratory reduction strategies in ICU.

OBJECTIVE: To build a machine-learning model that predicts laboratory test results and provides a promising lab test reduction strategy, using spatial-temporal correlations. MATERIALS AND METHODS: We developed a global prediction model to treat laboratory testing as a series of decisions by considering contextual information over time and across modalities. We validated our method using a critical care database (MIMIC III), which includes 4,570,709 observations of 12 standard laboratory tests, among 38,773 critical care patients. Our deep-learning model made real-time laboratory reduction recommendations and predicted the properties of lab tests, including values, normal/abnormal (whether labs were within the normal range) and transition (normal to abnormal or abnormal to normal from the latest lab test). We reported area under the receiver operating characteristic curve (AUC) for predicting normal/abnormal, evaluated accuracy and absolute bias on prediction vs. observation against lab test reduction proportion. We compared our model against baseline models and analyzed the impact of variations on the recommended reduction strategy. RESULTS: Our best model offered a 20.26 % reduction in the number of laboratory tests. By applying the recommended reduction policy on the hold-out dataset (7755 patients), our model predicted normality/abnormality of laboratory tests with a 98.27 % accuracy (AUC, 0.9885; sensitivity, 97.84 %; specificity, 98.80 %; PPV, 99.01 %; NPV, 97.39 %) on 20.26 % reduced lab tests, and recommended 98.10 % of transitions to be checked. Our model performed better than the greedy models, and the recommended reduction strategy was robust. DISCUSSION: Strong spatial and temporal correlations between laboratory tests can be used to optimize policies for reducing laboratory tests throughout the hospital course. Our method allows for iterative predictions and provides a superior solution for the dynamic decision-making laboratory reduction problem. CONCLUSION: This work demonstrates a machine-learning model that assists physicians in determining which laboratory tests may be omitted.

Recollecting Cross-Cultural Evidences: Are Decision Makers Really Foresighted in Iowa Gambling Task?

The Iowa Gambling Task (IGT) has become a remarkable experimental paradigm of dynamic emotion decision making. In recent years, research has emphasized the "prominent deck B (PDB) phenomenon" among normal (control group) participants, in which they favor "bad" deck B with its high-frequency gain structure-a finding that is incongruent with the original IGT hypothesis concerning foresightedness. Some studies have attributed such performance inconsistencies to cultural differences. In the present review, 86 studies featuring data on individual deck selections were drawn from an initial sample of 958 IGT-related studies published from 1994 to 2017 for further investigation. The PDB phenomenon was found in 67.44% of the studies (58 of 86), and most participants were recorded as having adopted the "gain-stay loss-randomize" strategy to cope with uncertainty. Notably, participants in our sample of studies originated from 16 areas across North America, South America, Europe, Oceania, and Asia, and the findings suggest that the PDB phenomenon may be cross-cultural.

Basic Processes in Dynamic Decision Making: How Experimental Findings About Risk, Uncertainty, and Emotion Can Contribute to Police Decision Making.

In this paper, we review basic findings from experimental studies in judgment and decision making that could contribute to designing policies and trainings to enhance police decision making. Traditional judgment and decision-making research has focused on simple choices between hypothetical gambles, which has been criticized for its lack of generalizability to real world contexts. Over the past 15 years, researchers have focused on understanding the dynamic processes in decision making. This recent focus has allowed for the possibility of more generalizable applications of basic decision science to social issues. We review recent work in three dynamic decision-making topics: dynamic accumulation of evidence in the decision to shoot or not shoot, how previous decisions influence current choices, and how the cognitive and neurological processing of fear influences decisions and decision errors. We conclude this review with a summary of how basic experimental research can apply in policing and training.

Make-or-Break: Chasing Risky Goals or Settling for Safe Rewards?

Humans regularly pursue activities characterized by dramatic success or failure outcomes where, critically, the chances of success depend on the time invested working toward it. How should people allocate time between such make-or-break challenges and safe alternatives, where rewards are more predictable (e.g., linear) functions of performance? We present a formal framework for studying time allocation between these two types of activities, and we explore optimal behavior in both one-shot and dynamic versions of the problem. In the one-shot version, we illustrate striking discontinuities in the optimal time allocation policy as we gradually change the parameters of the decision-making problem. In the dynamic version, we formulate the optimal strategy-defined by a giving-up threshold-which adaptively dictates when people should stop pursuing the make-or-break goal. We then show that this strategy is computationally inaccessible for humans, and we explore boundedly rational alternatives. We compare the performance of the optimal model against (a) a myopic giving-up threshold that is easier to compute, and even simpler heuristic strategies that either (b) only decide whether or not to start pursuing the goal and never give up or (c) consider giving up at a fixed number of control points. Comparing strategies across environments, we investigate the cost and behavioral implications of sidestepping the computational burden of full rationality.