Model Of Human Decision Research Articles

Nudging human drivers via implicit communication by automated vehicles: Empirical evidence and computational cognitive modeling

Understanding behavior of human drivers in interactions with automated vehicles (AV) can aid the development of future AVs. Existing investigations of such behavior have predominantly focused on situations in which an AV a priori needs to take action because the human has the right of way. However, future AVs might need to proactively manage interactions even if they have the right of way over humans, e.g., a human driver taking a left turn in front of the approaching AV. Yet it remains unclear how AVs could behave in such interactions and how humans would react to them. To address this issue, here we investigated behavior of human drivers (N = 19) when interacting with an oncoming AV during unprotected left turns in a driving simulator experiment. We measured the outcomes (Go or Stay) and timing of participants’ decisions when interacting with an AV which performed subtle longitudinal nudging maneuvers, e.g. briefly decelerating and then accelerating back to its original speed. We found that participants’ behavior was sensitive to deceleration nudges but not acceleration nudges. We compared the obtained data to predictions of several variants of a drift-diffusion model of human decision making. The most parsimonious model that captured the data hypothesized noisy integration of dynamic information on time-to-arrival and distance to a fixed decision boundary, with an initial accumulation bias towards the Go decision. Our model not only accounts for the observed behavior but can also flexibly generate predictions of human responses to arbitrary longitudinal AV maneuvers, and can be used for both informing future studies of human behavior and incorporating insights from such studies into computational frameworks for AV interaction planning.

Modelling dataset bias in machine-learned theories of economic decision-making

Normative and descriptive models have long vied to explain and predict human risky choices, such as those between goods or gambles. A recent study reported the discovery of a new, more accurate model of human decision-making by training neural networks on a new online large-scale dataset, choices13k. Here we systematically analyse the relationships between several models and datasets using machine-learning methods and find evidence for dataset bias. Because participants’ choices in stochastically dominated gambles were consistently skewed towards equipreference in the choices13k dataset, we hypothesized that this reflected increased decision noise. Indeed, a probabilistic generative model adding structured decision noise to a neural network trained on data from a laboratory study transferred best, that is, outperformed all models apart from those trained on choices13k. We conclude that a careful combination of theory and data analysis is still required to understand the complex interactions of machine-learning models and data of human risky choices.

Marketing automation and the scope of marketers' heuristics

PurposeThis paper examines the relationship between marketing automation emergence and the marketers' use of heuristics in their decision-making processes. Heuristics play a role for the integration of human decision-making models and automation in augmentation processes, particularly in marketing where automation is widespread.Design/methodology/approachThis study analyzes qualitative data about the impact of marketing automation on the scope of heuristics in decision-making models, and it is based on evidence collected from interviews with twenty-two experienced marketers.FindingsMarketers make extensive use of heuristics to manage their tasks. While the adoption of new automatic marketing tools modify the task environment and field of use of traditional decision-making models, the adoption of heuristics rules with a different scope is essential to defining inputs, interpreting/evaluating outputs and control the marketing automation system.Originality/valueThe paper makes a contribution to research on the relationship between marketing automation and decision-making models. In particular, it proposes the results of in-depth interviews with senior decision makers to assess the impact of marketing automation on the scope of heuristics as decision-making models adopted by marketers.

Quantum Circuit Components for Cognitive Decision-Making

This paper demonstrates that some non-classical models of human decision-making can be run successfully as circuits on quantum computers. Since the 1960s, many observed cognitive behaviors have been shown to violate rules based on classical probability and set theory. For example, the order in which questions are posed in a survey affects whether participants answer 'yes' or 'no', so the population that answers 'yes' to both questions cannot be modeled as the intersection of two fixed sets. It can, however, be modeled as a sequence of projections carried out in different orders. This and other examples have been described successfully using quantum probability, which relies on comparing angles between subspaces rather than volumes between subsets. Now in the early 2020s, quantum computers have reached the point where some of these quantum cognitive models can be implemented and investigated on quantum hardware, by representing the mental states in qubit registers, and the cognitive operations and decisions using different gates and measurements. This paper develops such quantum circuit representations for quantum cognitive models, focusing particularly on modeling order effects and decision-making under uncertainty. The claim is not that the human brain uses qubits and quantum circuits explicitly (just like the use of Boolean set theory does not require the brain to be using classical bits), but that the mathematics shared between quantum cognition and quantum computing motivates the exploration of quantum computers for cognition modeling. Key quantum properties include superposition, entanglement, and collapse, as these mathematical elements provide a common language between cognitive models, quantum hardware, and circuit implementations.

Expansion of fuzzy sets to account for doubts and uncertainty of experts based on a modified aggregation operator

Quantification in collective behavior and decision-making in fuzzy conditions is crucial to ensure the health and safety of the population. The task of modeling and predicting behavior in fuzzy conditions, as is known, has increased complexity due to a large number of factors from which an NP-complete multi-criteria problem is formed. There is a difficulty in quantifying the impact of fuzzy factors using a mathematical model. In this regard, the paper proposes a stochastic model of human decision- making to account for doubts and uncertainty of experts based on a modified aggregation operator. The developed fuzzy model combines fuzzy logic into a conventional model of social behavior. Unlike existing models and applications, this approach uses fuzzy sets and membership functions to describe the evacuation process in an emergency situation. The purpose of this work is to expand fuzzy sets to account for doubts and uncertainty of experts based on a modified aggregation operator and analysis of existing solutions. The problem statement in this paper is as follows: to form a set of fuzzy sets to account for doubts and uncertainty of experts based on a modified aggregation operator. The scientific novelty lies in the formation of a set of factors that form fuzzy rules for making dynamic decisions. The practical value of the work lies in the creation of fuzzy sets to account for doubts and uncertainty of experts based on a modified aggregation operator [1-3]. To implement the proposed model, the process of social behavior during evacuation, independent variables are determined. These variables include measurements related to social factors, in other words, the behavior of individual subjects and individual small groups, which are fundamental at an early stage of evacuation.

Assessing evidence accumulation and rule learning in humans with an online game.

Evidence accumulation, an essential component of perception and decision making, is frequently studied with psychophysical tasks involving noisy or ambiguous stimuli. In these tasks, participants typically receive verbal or written instructions that describe the strategy that should be used to guide decisions. Although convenient and effective, explicit instructions can influence learning and decision making strategies and can limit comparisons with animal models, in which behaviors are reinforced through feedback. Here, we developed an online video game and nonverbal training pipeline, inspired by pulse-based tasks for rodents, as an alternative to traditional psychophysical tasks used to study evidence accumulation. Using this game, we collected behavioral data from hundreds of participants trained with an explicit description of the decision rule or with experiential feedback. Participants trained with feedback alone learned the game rules rapidly and used strategies and displayed biases similar to those who received explicit instructions. Finally, by leveraging data across hundreds of participants, we show that perceptual judgments were well described by an accumulation process in which noise scaled nonlinearly with evidence, consistent with previous animal studies but inconsistent with diffusion models widely used to describe perceptual decisions in humans. These results challenge the conventional description of the accumulation process and suggest that online games provide a valuable platform to examine perceptual decision making and learning in humans. In addition, the feedback-based training pipeline developed for this game may be useful for evaluating perceptual decision making in human populations with difficulty following verbal instructions.NEW & NOTEWORTHY Perceptual uncertainty sets critical constraints on our ability to accumulate evidence and make decisions; however, its sources remain unclear. We developed a video game, and feedback-based training pipeline, to study uncertainty during decision making. Leveraging choices from hundreds of subjects, we demonstrate that human choices are inconsistent with popular diffusion models of human decision making and instead are best fit by models in which perceptual uncertainty scales nonlinearly with the strength of sensory evidence.

HKGAIL: Policy shaping via integrating human knowledge with generative adversarial imitation learning

AbstractGenerative adversarial imitation learning (GAIL) has shown good results in several research areas by taking advantage of generative adversarial networks. However, GAIL lacks a reward mechanism and usually adopts a model‐free approach based on stochastic policies, which is not ideal for solving complex, dynamically uncertain population intelligence problems, especially in the face of autonomous driving environments. In this paper, a policy framework is shaped by combining the human knowledge with GAIL (HKGAIL). HKGAIL embeds human decision models into the learning process to infer the underlying structure of expert demonstrations. The skills learned from expert demonstrations can directly guide the actions (policies) of the learning process of the agents, and the policies can be optimized through the feedback function of the discriminator. Experiments on both driving and landing tasks show that HKGAIL was able to better fit the policy close to the expert, and was 16.2% safer than GAIL for the driving task.

Coupled biophysical and decision-making processes in grassland systems in East African savannahs – A modelling framework

Increasing livestock densities on limited grazing areas in African savannahs lead to resource degradation through overgrazing, aggravated by drought. Assessing herd management strategies over longer periods at landscape scale is important to propose options for sustainable land use. This requires an understanding of processes related to hydrology, nutrient cycling, herd movement, pasture degradation, and animal resilience that involve dynamic soil-plant-animal interactions and human decisions about stocking rates, livestock purchases and sales.We present the coupled model system MPMAS-LUCIA-LIVSIM (MLL), the combination of a spatially explicit agent-based model for human decision-making (MPMAS), a spatially distributed landscape model for water flows, nutrient cycles and plant growth (LUCIA), and a herd model (LIVSIM) representing grazing, body weight, nutrition and excreta of individual animals. MLL represents daily vegetation growth in response to grazing and organic inputs, monthly animal performance influenced by forage availability and quality, and herders’ management in response to resource status. New modules for selective grazing, resprouting of pasture, herd movement and model coupling were developed for MLL.The test case of a pastoral system in the Ethiopian Borana region demonstrates the capabilities of MLL to simulate key soil-plant-animal-human interactions under climate-related management scenarios with varying access to grazing land, changing cattle prices and different spending / saving behaviour of herders. 20-year simulations showed the negative impact of consecutive drought years on vegetation biomass, on herd development and movement and how reserving grazing areas for dry seasons could mitigate overgrazing and improve income. Seasonality and drought response of vegetation growth, selective grazing of different plant parts, resprouting after grazing, calving intervals, milk yields and lactation in response to forage supply and quality as well as herder reactions to shocks were plausibly represented.Building upon this successful proof-of-concept, MLL can be used to identify robust management options for improved grazing systems in savannahs in follow-up research.

Heuristics from bounded meta-learned inference.

Numerous researchers have put forward heuristics as models of human decision-making. However, where such heuristics come from is still a topic of ongoing debate. In this work, we propose a novel computational model that advances our understanding of heuristic decision-making by explaining how different heuristics are discovered and how they are selected. This model-called bounded meta-learned inference (BMI)-is based on the idea that people make environment-specific inferences about which strategies to use while being efficient in terms of how they use computational resources. We show that our approach discovers two previously suggested types of heuristics-one reason decision-making and equal weighting-in specific environments. Furthermore, the model provides clear and precise predictions about when each heuristic should be applied: Knowing the correct ranking of attributes leads to one reason decision-making, knowing the directions of the attributes leads to equal weighting, and not knowing about either leads to strategies that use weighted combinations of multiple attributes. In three empirical paired comparison studies with continuous features, we verify predictions of our theory and show that it captures several characteristics of human decision-making not explained by alternative theories. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Development of human decision making model with consideration of human factors through reinforcement learning and prospect utility theory

ABSTRACT Human decision-making (HDM) is a complex process. Various human factors play a significant role in this process. Human factors directly or indirectly affect the entire process of decision-making (DM). In this study, an attempt has been made to integrate some of the human factors like past experiences (pe), emotion (ef), times factors (tf), and uncertain (un) with the reinforcement learning (RL) method to develop model for HDM. For this Iowa gambling Task (IGT) has been used as a data collection tool, data of 57 subjects were collected. It is a well-known experience-based task that helps to identify the DM behaviour of participants. An AHP method has been also used to decide the criteria weight to different human factors in the HDM model. Four learning models are developed that are the combination of different utility functions, learning rules, and choice rules. The AHP method decides the preference of various factors incorporated in the developed models. From the results, it is observed that the model based on prospect utility, decay RL, and trial dependency (PU-DRI-TDC Model) performs better when the emotion factor was given the highest preference than others. In addition to this, the IGT learning of participants was also analysed.

An investigation of coupled natural human systems using a two-way coupled agent-based modeling framework

Improving the understanding of coupled natural human systems (CNHS) can better inform environmental policymaking. We investigated the co-evolution (i.e., bidirectional interactions) issues in CNHS via two-way coupling RiverWare (RW; a river-reservoir routing model) with agent-based models (ABMs, human decision models) in the Yakima River Basin in Washington, US. Results show that coupled models can better capture the historical irrigation diversion (human) and streamflow (nature) dynamics. We further demonstrated the effect of social norms (i.e., the influence of neighbors) among farmers and tested a “water reallocation” scenario to evaluate the influence of water policies on irrigation diversion behaviors. Detailed model structure and parameter uncertainty analysis are suggested to further quantify the benefit of CNHS models in multi-level water resources governance.

The Effects of Model Complexity on Model Output Uncertainty in Co‐Evolved Coupled Natural‐Human Systems

AbstractStudies have recently focused on using coupled natural‐human systems (CNHS) to inform policymaking. However, model uncertainty can increase with model complexity and affect the variance of the model outcomes. Therefore, this study explores an uncertainty analysis of coupled hydrological and human decision models to better evaluate CNHS modeling properties. Five coupled models are proposed with different model complexities for human behavior settings (i.e., model structure and the number of calibrated parameters): one static, two adaptive, and two learning adaptive. Learning adaptive models (the most complex) have both a learning component (capturing long‐term trends) and an adaptive component (capturing short‐term variations), while adaptive models omit the learning component. The static model is the simplest, without learning or adaptive components. Applying the law of total variance, the model output uncertainty is decomposed into three sources: (a) climate change scenario uncertainty, (b) climate internal variability, and (c) different model configurations with parameter sets or model structures that are equally capable of producing similar outcomes. Our exploratory analysis demonstrated that model uncertainty would likely increase with model complexity given uncertain input data (e.g., climate forcing) and different model configurations; the inclusion of a learning mechanism in the human system can potentially offset the impact of the natural system on uncertainty through coupling natural and human systems. We also discuss other uncertainty sources, such as assumptions about model structure due to incomplete knowledge and metrics for calibration target selection for future studies.

Collaborative Human Decision Making With Heterogeneous Agents

While there has been extensive work on modeling of human decision-making both for individuals and groups from a cognitive psychology point of view, research on this topic from a signal processing and information fusion perspective is relatively recent. In this work, we consider a distributed detection problem consisting of a number of human local decision makers and a fusion center (FC). Signal detection theory is exploited to answer why promoting heterogeneity could improve the performance of collaborative human decision-making. We consider the following two scenarios: 1) the local decision makers are independent and the level of heterogeneity is measured in terms of the variability of human expertise and 2) humans make correlated local decisions due to their perceptual and behavioral similarities and heterogeneity is measured by the amount of correlation. In both cases, we show that the detection performance of the FC can be improved with the increase of heterogeneity. In particular, in the second scenario, we develop a portfolio theory-based framework to select participants from correlated human agents so that heterogeneity is enhanced resulting in improved decision-making performance. Simulations are provided for illustration and performance comparison.

Developing Fire Evacuation Simulation Through Emotion-based BDI Methodology

Fire evacuation simulation is a tool to study human behavior in dealing with fire. It has been used for safety policy management studies, building safety analysis, and human safety understanding. To date, modeling the fire evacuation behavior is paying much attention in which works have been done to design and develop building model, fire model, human decision model, and human emotion decision model. As fire evacuation simulation is important, the BDI methodology is introduced by authors to ease modeling and simulation of human behavior in a fire evacuation. Continue the success of capturing and modeling the human behavior in a fire evacuation. This paper presents the influence of human emotion in fire evacuation simulation. In this paper, the emotion-based BDI methodology is presented with a walkthrough example of how emotion can influence the human decision in a fire spreading scenario. The OCEAN model of personality is used to handle the emotional properties in the methodology. Different people have different types of personalities, which can affect both decision-making and emotion in different situations. A fire evacuation simulation is developed by using the Unity3D game engine. The simulation is created based on the emotion-based BDI methodology presented. Hence, the emotion-based BDI methodology can be used to model human behavior and emotional states in a fire evacuation. Overall, the paper introduces a new insight into how to model human behavior in fire evacuation decision-making systematically.

Teaching & learning guide for: Risky‐choice framing and rational decision‐making

Teaching & learning guide for: Risky‐choice framing and rational decision‐making

Cognitive Science as a Source of Forward and Inverse Models of Human Decisions for Robotics and Control

Those designing autonomous systems that interact with humans will invariably face questions about how humans think and make decisions. Fortunately, computational cognitive science offers insight into human decision-making using tools that will be familiar to those with backgrounds in optimization and control (e.g., probability theory, statistical machine learning, and reinforcement learning). Here, we review some of this work, focusing on how cognitive science can provide forward models of human decision-making and inverse models of how humans think about others’ decision-making. We highlight relevant recent developments, including approaches that synthesize black box and theory-driven modeling, accounts that recast heuristics and biases as forms of bounded optimality, and models that characterize human theory of mind and communication in decision-theoretic terms. In doing so, we aim to provide readers with a glimpse of the range of frameworks, methodologies, and actionable insights that lie at the intersection of cognitive science and control research.

Variable-Drift Diffusion Models of Pedestrian Road-Crossing Decisions

Human behavior and interaction in road traffic is highly complex, with many open scientific questions of high applied importance, not least in relation to recent development efforts toward automated vehicles. In parallel, recent decades have seen major advances in cognitive neuroscience models of human decision-making, but these models have mainly been applied to simplified laboratory tasks. Here, we demonstrate how variable-drift extensions of drift diffusion (or evidence accumulation) models of decision-making can be adapted to the mundane yet non-trivial scenario of a pedestrian deciding if and when to cross a road with oncoming vehicle traffic. Our variable-drift diffusion models provide a mechanistic account of pedestrian road-crossing decisions, and how these are impacted by a variety of sensory cues: time and distance gaps in oncoming vehicle traffic, vehicle deceleration implicitly signaling intent to yield, as well as explicit communication of such yielding intentions. We conclude that variable-drift diffusion models not only hold great promise as mechanistic models of complex real-world decisions, but that they can also serve as applied tools for improving road traffic safety and efficiency.

A Competition of Critics in Human Decision-Making.

Recent experiments and theories of human decision-making suggest positive and negative errors are processed and encoded differently by serotonin and dopamine, with serotonin possibly serving to oppose dopamine and protect against risky decisions. We introduce a temporal difference (TD) model of human decision-making to account for these features. Our model involves two critics, an optimistic learning system and a pessimistic learning system, whose predictions are integrated in time to control how potential decisions compete to be selected. Our model predicts that human decision-making can be decomposed along two dimensions: the degree to which the individual is sensitive to (1) risk and (2) uncertainty. In addition, we demonstrate that the model can learn about the mean and standard deviation of rewards, and provide information about reaction time despite not modeling these variables directly. Lastly, we simulate a recent experiment to show how updates of the two learning systems could relate to dopamine and serotonin transients, thereby providing a mathematical formalism to serotonin's hypothesized role as an opponent to dopamine. This new model should be useful for future experiments on human decision-making.

Information Theory for Agents in Artificial Intelligence, Psychology, and Economics.

This review looks at some of the central relationships between artificial intelligence, psychology, and economics through the lens of information theory, specifically focusing on formal models of decision-theory. In doing so we look at a particular approach that each field has adopted and how information theory has informed the development of the ideas of each field. A key theme is expected utility theory, its connection to information theory, the Bayesian approach to decision-making and forms of (bounded) rationality. What emerges from this review is a broadly unified formal perspective derived from three very different starting points that reflect the unique principles of each field. Each of the three approaches reviewed can, in principle at least, be implemented in a computational model in such a way that, with sufficient computational power, they could be compared with human abilities in complex tasks. However, a central critique that can be applied to all three approaches was first put forward by Savage in The Foundations of Statistics and recently brought to the fore by the economist Binmore: Bayesian approaches to decision-making work in what Savage called ‘small worlds’ but cannot work in ‘large worlds’. This point, in various different guises, is central to some of the current debates about the power of artificial intelligence and its relationship to human-like learning and decision-making. Recent work on artificial intelligence has gone some way to bridging this gap but significant questions remain to be answered in all three fields in order to make progress in producing realistic models of human decision-making in the real world in which we live in.

Dynamic Inconsistency and Incentive Design: Insights from Behavioural Economics for HR Managers

Behavioural economics is a thriving field that offers descriptive models of human decision-making that deviate from the standard model of decision-making in economics. This article presents insights from behavioural economics that can help address dynamic inconsistency, that is, time-inconsistency problems of employees and inform incentive design strategies. The author argues that lessons from behavioural economics can be applied to design solutions that can transform HR practices. HR managers and leaders stand to benefit from the emerging evidence from the lab and field in behavioural economics that calls for a rethinking of the conventional understanding of human behaviour.