Towards a Relevance-Theoretic Model of Decision-Making in Subtitling

Market integration (MI) is a complex process through which individuals transition from relatively subsistence-based to market-oriented activities. Changes associated with MI alter the landscapes of individual health and reproductive decision-making. While the consequences of MI are often easily detected, the specific pathways through which MI affects decision-making are context-dependent and under-investigated. We employed an information-theoretic model selection approach to characterize relationships between multiple indicators of MI and three outcomes commonly associated with MI, waist circumference (n = 431), systolic blood pressure (n = 472), and age at first reproduction (n = 974), among adult matrilineal Mosuo participants from 505 households in six villages in southwest China. Different MI indicators, distributed across individual, household, and community levels of social organization, predicted these three outcomes, demonstrating that individuals’ personal circumstances, household structure, and community affect how they experience and respond to MI. We emphasize the importance of identifying and measuring multiple context-appropriate indicators of MI across levels of social organization. Theoretical frameworks that situate hypotheses of MI within specific social, cultural, and historical contexts will be most capable of identifying specific pathways through which multiple elements of MI affect different domains of decision making.

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Information Theoretic Model to Simulate Agent-Signage Interaction for Wayfinding

Cognitive Computation ◽

10.1007/s12559-019-09689-1 ◽

2019 ◽

Cited By ~ 1

Author(s):

Rohit K. Dubey ◽

Tyler Thrash ◽

Mubbasir Kapadia ◽

Christoph Hoelscher ◽

Victor R. Schinazi

Keyword(s):

Decision Making ◽

Spatial Information ◽

Interaction Model ◽

Training Dataset ◽

Observation Angle ◽

Theoretic Model ◽

Viewing Distance ◽

Crowd Sourcing ◽

Signage System ◽

Perceived Complexity

AbstractSignage systems are critical for communicating spatial information during wayfinding among a plethora of noise in the environment. A proper signage system can improve wayfinding performance and user experience by reducing the perceived complexity of the environment. However, previous models of sign-based wayfinding do not incorporate realistic noise or quantify the reduction in perceived complexity from the use of signage. Drawing upon concepts from information theory, we propose and validate a new agent-signage interaction model that quantifies available wayfinding information from signs for wayfinding. We conducted two online crowd-sourcing experiments to compute the distribution of a sign’s visibility and an agent’s decision-making confidence as a function of observation angle and viewing distance. We then validated this model using a virtual reality (VR) experiment with trajectories from human participants. The crowd-sourcing experiments provided a distribution of decision-making entropy (conditioned on visibility) that can be applied to any sign/environment. From the VR experiment, a training dataset of 30 trajectories was used to refine our model, and the remaining test dataset of 10 trajectories was compared with agent behavior using dynamic time warping (DTW) distance. The results revealed a reduction of 38.76% in DTW distance between the average trajectories before and after refinement. Our refined agent-signage interaction model provides realistic predictions of human wayfinding behavior using signs. These findings represent a first step towards modeling human wayfinding behavior in complex real environments in a manner that can incorporate several additional random variables (e.g., environment layout).

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Attribute Reduction Algorithm on Balancing Profit and Risk

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.411-414.1919 ◽

2013 ◽

Vol 411-414 ◽

pp. 1919-1922 ◽

Cited By ~ 1

Author(s):

De Xing Wang ◽

Jie Long Xu ◽

Yun Zhang

Keyword(s):

Decision Making ◽

Heuristic Search ◽

Social Life ◽

Search Algorithm ◽

Attribute Reduction ◽

Theoretic Model ◽

Minimal Risk ◽

Economic Activities ◽

Heuristic Search Algorithm ◽

Maximal Profit

Usually it is taken grant that we achieve the maximal profit and the minimal risk in industry, agriculture, economic activities and social life. It is an important problem in a decision-making process on how to balance profit and risk and find out practical decision-making ways. This paper builds a decision-theoretic model which can balance profit and risk and provide a heuristic search algorithm of the attribute reduction. This algorithm takes the profit and cost as the heuristic function and outputs an optimal attribute set. At last, the example shows that the proposed algorithm is correct and efficient.

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Understanding Design Decisions Under Competition Using Games With Information Acquisition and a Behavioral Experiment

Journal of Mechanical Design ◽

10.1115/1.4037253 ◽

2017 ◽

Vol 139 (9) ◽

Cited By ~ 11

Author(s):

Jitesh H. Panchal ◽

Zhenghui Sha ◽

Karthik N. Kannan

Keyword(s):

Decision Making ◽

Information Acquisition ◽

Analytical Models ◽

Behavioral Experiment ◽

Theoretic Model ◽

Design Decision ◽

Behavioral Experiments ◽

Design Decisions ◽

Game Theoretic

The primary motivation in this paper is to understand decision-making in design under competition from both prescriptive and descriptive perspectives. Engineering design is often carried out under competition from other designers or firms, where each competitor invests effort with the hope of getting a contract, attracting customers, or winning a prize. One such scenario of design under competition is crowdsourcing where designers compete for monetary prizes. Within existing literature, such competitive scenarios have been studied using models from contest theory, which are based on assumptions of rationality and equilibrium. Although these models are general enough for different types of contests, they do not address the unique characteristics of design decision-making, e.g., strategies related to the design process, the sequential nature of design decisions, the evolution of strategies, and heterogeneity among designers. In this paper, we address these gaps by developing an analytical model for design under competition, and using it in conjunction with a behavioral experiment to gain insights about how individuals actually make decisions in such scenarios. The contributions of the paper are two-fold. First, a game-theoretic model is presented for sequential design decisions considering the decisions made by other players. Second, an approach for synergistic integration of analytical models with data from behavioral experiments is presented. The proposed approach provides insights such as shift in participants' strategies from exploration to exploitation as they acquire more information, and how they develop beliefs about the quality of their opponents' solutions.

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Awareness Growth and Belief Revision

10.31235/osf.io/bfv56 ◽

2021 ◽

Author(s):

Joe Roussos

Keyword(s):

Decision Making ◽

Belief Revision ◽

Algebra I ◽

Rational Belief ◽

Theoretic Model ◽

Bayesian Approaches ◽

Revision Procedure ◽

New Information ◽

The Face ◽

Awareness Change

The problem of awareness growth, also known as the problem of new hypotheses, is a persistent challenge to Bayesian theories of rational belief and decision making. Cases of awareness growth include coming to consider a completely new possibility (called expansion), or coming to consider finer distinctions through the introduction of a new partition (called refinement). Recent work has centred on Reverse Bayesianism, a proposal for rational awareness growth due to Karni and Vierø. This essay develops a "Reserve Bayesian" position and defends it against two challenges. The first, due to Anna Mahtani, says that Reverse Bayesian approaches yield the wrong result in cases where the growth of awareness constitutes an expansion relative to one partition, but a refinement relative to a different partition. The second, due to Steele and Stefánsson, says that Reverse Bayesian approaches cannot deal with new propositions that are evidentially relevant to old propositions. I argue that these challenges confuse questions of belief revision with questions of awareness change. Mahtani’s cases reveal that the change of awareness itself requires a model which specifies how propositions in the agent’s old algebra are identified with propositions in the new algebra. I introduce a lattice-theoretic model for this purpose, which resolves Mahtani’s problem cases and some of Steele and Stefánsson’s cases. Applying my model of awareness change, then Reverse Bayesianism, and then a generalised belief revision procedure, resolves Steele and Stefánsson’s remaining cases. In demonstrating this, I introduce a simple and general model of belief revision in the face of new information about previously unknown propositions.

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Nuclear Deterrence in the Algorithmic Age: Game Theory Revisited

NL ARMS - NL ARMS Netherlands Annual Review of Military Studies 2020 ◽

10.1007/978-94-6265-419-8_22 ◽

2020 ◽

pp. 421-436

Author(s):

Roy Lindelauf

Keyword(s):

Game Theory ◽

Decision Making ◽

Decision Maker ◽

Cognitive Biases ◽

Autonomous Systems ◽

Nuclear Deterrence ◽

Theoretic Model ◽

Nuclear Strategy ◽

Strategic Stability ◽

Decision Making Processes

AbstractCommonly used game and decision theoretic models fail to explain the empirics of deterrence. This has unjustly led many theorists to criticize the (rationality and other) assumptions underpinning of such models. No serious game theorist will contend that his theoretic model will possibly take account of all the peculiarities involved in decision making and therefore be an accurate model of such situations. Games are an aid to thinking about some of the aspects of the broader situation. Game theory models prescribe what a decision maker ought to do in a given situation, not what a decision maker actually does. To maintain nuclear strategic stability, it is of paramount importance to understand the dynamical interplay between all players involved in decision making processes with regard to nuclear strategy. History has shown some progress in understanding nuclear deterrence by the use of initial game- and decision theoretic models to alleviate the burden of human cognitive biases. Since it is highly likely that (semi-)autonomous systems will in some way participate in the future nuclear strategic landscape, combined with the fact that the nuclear deterrent decision-cycle will also be based on algorithmic analysis, rational deterrence theory is and should be an integral element of strategic thinking about nuclear deterrence. That, or it might as well be game over.

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INTRODUCING A THEORETIC MODEL AND AN EMPIRIC NORM FOR INFORMATION RISK MANAGEMENT IN DECISION MAKING

Problems of Management in the 21st Century ◽

10.33225/pmc/17.12.39 ◽

2017 ◽

Vol 12 (1) ◽

pp. 39-53

Author(s):

Stefan Schwerd ◽

Richard Mayr

Keyword(s):

Decision Making ◽

Risk Management ◽

Information Source ◽

Computer Mediated Communication ◽

High Volume ◽

Theoretic Model ◽

Mediated Communication ◽

Information Risk ◽

Level Information ◽

Increasing Demand

Nowadays computer mediated communication (CMC) and the high volume of computed and stored information is getting a business on its own. Information is collected, aggregated, analyzed and used to create real business advantage and value but also risks within companies and also outside on the markets in a high volume. On the other hand, single individuals still need to deal and interpret this sheer mass of increasing information continuously. The change in information management and handling triggers the ongoing changes in decision makings on the operational level as well as on the strategic level. Information is a good sold itself and triggered an own industry of information brokerage. It opens the question of trust and correctness into the information itself but also into the information source and opens a complete new, not modelled yet discipline of Information Risk Management. Currently no model exists in science to measure Information Risk Management where as there is a highly increasing demand to measure case-based applicability and success of Information Risk-Management (IRM) activities in a broader context. The authors propose a new model for IRM and derive a qualitative prove of variables/measure and a quantitative empiric-norm as a base for further perception comparison with specifically targeted groups. Keywords: information risk management, management theory, decision making, enterprise risk management.

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Clinical Decision-Making: Developing a 4 C Model Using Graph Theoretic Approach

Studies in Engineering and Technology ◽

10.11114/set.v7i1.4781 ◽

2020 ◽

Vol 7 (1) ◽

pp. 30

Author(s):

Rajesh P. Mishra ◽

Nidhi Mundra ◽

Girish Upreti ◽

Marcela Villa-Marulanda

Keyword(s):

Decision Making ◽

Clinical Decision Making ◽

Clinical Decision ◽

Theoretic Model ◽

Specific Situation ◽

Theoretic Approach ◽

Graph Theoretic ◽

Treatment Processes ◽

Graph Theoretic Approach ◽

Major Factors

The purpose of this paper is to propose a graph-theoretic mathematical model to measure how conducive the environment of a hospital is for decision-making. We propose a 4-C model, developed from four interacting factors: confidence, complexity, capability, and customer. In this graph-theoretic model, abstract information regarding the system is represented by the directed edges of a graph (or digraph), which together depict how one factor affects another. The digraph yields a matrix model useful for computer processing. The net effect of different factors and their interdependencies on the hospital's decision-making environment is quantified and a single numerical index is generated. This paper categorizes all the major factors that influence clinical decision-making and attempts to provide a tool to study and measure their interactions with each other. Each factor and each interaction among factors are to be quantified by healthcare experts according to their best judgment of the magnitude of its effect in a local hospital environment.A hospital case study is used to demonstrate how the 4-C model works. The graph-theoretic approach allows for the inclusion of new factors and generation of alternative environments by a combination of both qualitative and quantitative modeling. The 4-C model can be used to create both a database and a simple numerical scale that help a hospital set customized guidelines, ranging from patient admittance procedures to diagnostic and treatment processes, according to its specific situation. Implementing this methodology systematically can allow a hospital to identify factors that will lead to improved decision-making as well as identifying operational factors that present roadblocks.

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Human-like Interactive Behavior Generation for Autonomous Vehicles: A Bayesian Game-theoretic Approach with Turing Test

10.22541/au.163482630.00283278/v1 ◽

2021 ◽

Author(s):

Yiran Zhang ◽

Peng Hang ◽

Chao Huang ◽

Chen Lv

Keyword(s):

Decision Making ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Turing Test ◽

Theoretic Model ◽

Theoretic Approach ◽

Bayesian Game ◽

Vehicle Simulation ◽

Road Users ◽

Game Theoretic

Interacting with surrounding road users is a key feature of vehicles and is critical for intelligence testing of autonomous vehicles. The Existing interaction modalities in autonomous vehicle simulation and testing are not sufficiently smart and can hardly reflect human-like behaviors in real world driving scenarios. To further improve the technology, in this work we present a novel hierarchical game-theoretical framework to represent naturalistic multi-modal interactions among road users in simulation and testing, which is then validated by the Turing test. Given that human drivers have no access to the complete information of the surrounding road users, the Bayesian game theory is utilized to model the decision-making process. Then, a probing behavior is generated by the proposed game theoretic model, and is further applied to control the vehicle via Markov chain. To validate the feasibility and effectiveness, the proposed method is tested through a series of experiments and compared with existing approaches. In addition, Turing tests are conducted to quantify the human-likeness of the proposed algorithm. The experiment results show that the proposed Bayesian game theoretic framework can effectively generate representative scenes of human-like decision-making during autonomous vehicle interactions, demonstrating its feasibility and effectiveness. Corresponding author(s) Email: [email protected]

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