General Decision Model Research Articles

Optimal Control and Signaling Strategies of Control-Coding Capacity of General Decision Models: Applications to Gaussian Models and Decentralized Strategies

Optimal Control and Signaling Strategies of Control-Coding Capacity of General Decision Models: Applications to Gaussian Models and Decentralized Strategies

An Evolutionary Framework for Modelling Unknown Behaviours of Other Agents

Multiagent decision making becomes complicated when a subject agent interacts with other agents who could be either collaborative or competitive in their common environment. Generally the subject agent does not know what exact behaviours other agents will execute in the coming interactions. This occurs even when the agents are collaborative and cannot fully disclose their behaviours due to their privacy concerns. With the purpose of improving prediction of the true behaviours of other agents, conventional approaches often employ either an exhaust list or a guided search to expand the set of possible behaviours of other agents. In this article, we investigate an evolutionary approach guided by prior knowledge about other agents and develop a genetic algorithm based framework to modelling unknown behaviours, which may contain the true behaviours of other agents. We develop a new framework with a multi-population genetic algorithm in a general multiagent decision model - interactive dynamic influence diagrams, which represents how multiple agents optimise their interactive decisions from the viewpoint of individual agents. The new framework also provides an alternative way to solving the decision model. We demonstrate the performance of the proposed methods in two problem domains and provide empirical results in support.

Behavior Decision Model With Situation Assessment for Intelligent Vehicles Based on Vehicle-to-Everything Information

Under the application of cooperative vehicle infrastructure technology, vehicles can drive more safely and efficiently in urban traffic environments. This paper presents a behavior decision model composed of a situation assessment module and decision process module to make a decision on lane selection and velocity control. Different common traffic elements are considered, such as surrounding obstacles, traffic lights, lane lines, and road velocity limits, to ensure the model outputs safe and legal behaviors. A green-wave passage strategy is also proposed to allow vehicles to pass through traffic intersections at a smoother velocity and to improve driving efficiency. To verify the correctness and rationality of the model, a Prescan/Simulink co-simulation platform including multiple common urban driving scenarios is established. The results show that the model can output correct behaviors with safety and legitimacy in different scenarios and realize vehicles meeting at intersections, vehicle-following, queuing at intersections with traffic lights, lane-changing for obstacle avoidance, and so forth. Compared with the general decision model without the green-wave passage strategy, the driving time is reduced by an average of 8.53%, and the standard deviation of the driving velocity is reduced by an average of 65.42%, which means that the model can reduce the variation of velocity and improve driving efficiency. The model has a certain practical application value.

General three-way decision models on incomplete information tables

In this paper, we propose general three-way decision models on incomplete information tables. First, for an incomplete information table, we give an axiomatic definition of similarity degree functions on a single attribute. By use of extended aggregation functions, similarity degree functions on an attribute set are also proposed. Then we define a new kind of similarity class of objects and study its properties. On the basis of this similarity class, general three-way decision models based on two evaluation functions and one evaluation function on incomplete information tables are established, respectively. In addition, we study the properties of these general three-way decision models. Finally, we compare the general model based on one evaluation function and a pair of thresholds with four existing models. The results show that the four existing models can be regarded as special cases of this general model, which illustrates the rationality of the new proposed models.

Kirsch\u2019s, and everyone\u2019s, bind: How to build models for the wild?

Alexandra Kirsch proposed a general formal model of decision making. She proposed it as a model both of human psychology and of artificial intelligence. As one might expect, and as Don Ross explicated, this is a challenging, albeit fascinating, position to occupy. In this comment, I sketch my own view of the bind and speculate on how to get out of it. In one sentence, my description of the bind is: How to build models for the wild? By models, I mean formal (mathematical, computer-based, precise conceptual) models, and by the wild, I mean “large worlds,” which are situations where uncertainty cannot be meaningfully reduced to well-developed devices such as probability. I discuss solutions for getting out of the bind proposed in the cognitive science of decision making. And I discuss how another discipline, operations research, has attempted to get out of the bind.

Ewaluacja ryzyka w zarządzaniu kryzysowym

<p>W artykule przedstawiono koncepcję metodologicznych podstaw ewaluacji ryzyka w sytuacjach zagrożeń bezpieczeństwa systemu. Przedstawiono ogólny model decyzyjny w warunkach niepewności i ryzyka oraz statystyczne miary ryzyka na potrzeby zarządzania przedsięwzięciami. Omówiono proces ewaluacji ryzyka w systemie zarządzania kryzysowego. W artykule zamieszczono przykłady modeli matematycznych ewaluacji ryzyka.</p>

A Generic Model for Follicular Lymphoma: Predicting Cost, Life Expectancy, and Quality-Adjusted-Life-Year Using UK Population–Based Observational Data

ObjectivesTo use real-world data to develop a flexible generic decision model to predict cost, life expectancy, and quality-adjusted life-years (QALYs) for follicular lymphoma (FL) in the general patient population. MethodsAll patients newly diagnosed with FL in the UK’s population-based Haematological Malignancy Research Network (www.hmrn.org) between 2004 and 2011 were followed until 2015 (N = 740). Treatment pathways, QALYs, and costs were incorporated into a discrete event simulation to reflect patient heterogeneity, including age and disease management. Two scenario analyses, based on the latest National Institute for Health and Clinical Excellence (NICE) guidelines (rituximab induction therapy for newly diagnosed asymptomatic patients and rituximab maintenance therapy for patients between treatments), were conducted and their economic impacts were compared to current practice. ResultsIncidence-based analysis revealed expected average lifetime costs ranging from £6,165 [US$7,709] to £63,864 [US$79,862] per patient, and average life expectancy from 75 days to 17.56 years. Prevalence-based analysis estimated average annual treatment costs of £60–65 million [US$75-80 million], accounting for approximately 10% of the United Kingdom’s annual National Health Service budget for hematological cancers as a whole. Assuming that treatment effects reported in trials are applicable to all patient groups, scenario analyses for two recent NICE guidelines demonstrated potential annual cost savings for the United Kingdom that ranged with uptake frequency from £0.6 million to £11 million [US$0.75-2.75 million]. ConclusionsCosts, survival, and QALYs associated with FL vary markedly with patient characteristics and disease management. Allowing the production of more realistic outcomes across the patient population as a whole, our model addresses this heterogeneity and is a useful tool with which to evaluate new technologies/treatments to support healthcare decision makers.

Processing Technology Selection for Municipal Sewage Treatment Based on a Multi-Objective Decision Model under Uncertainty.

This study considers the two factors of environmental protection and economic benefits to address municipal sewage treatment. Based on considerations regarding the sewage treatment plant construction site, processing technology, capital investment, operation costs, water pollutant emissions, water quality and other indicators, we establish a general multi-objective decision model for optimizing municipal sewage treatment plant construction. Using the construction of a sewage treatment plant in a suburb of Chengdu as an example, this paper tests the general model of multi-objective decision-making for the sewage treatment plant construction by implementing a genetic algorithm. The results show the applicability and effectiveness of the multi-objective decision model for the sewage treatment plant. This paper provides decision and technical support for the optimization of municipal sewage treatment.

Decision Procedure for Efficient Data Acquisition for Distinct Energy Prognosis Models

The current challenges for enterprises are heavily linked to resource and energy efficiency, a result of political guidelines and the overall consciousness for environmental issues in society. Thus, the impact of energy efficiency on factory planning and production planning is of undeniable importance. Adequate consumption information utilising various depiction methods has become a necessity in this respect. Yet, only few works provide information on the efficiency of energy data acquisition methods. This paper proposes a methodological framework for defining an efficient procedure for energy data acquisition in accordance with the depiction or prognosis method. A preliminary literature review indicates the variety of methods for energy data depiction and prognosis. The classification of these methods regarding planning state and planning levels leads to the estimation of relevant input data. Based on the fundamental investigation of necessary input data the decision approach is developed with a general heuristic decision model and incorporates the Analytic Hierarchy Process for quantification and solution of the overall decision problem. Therefore the integration of multiple decision criteria enables the consideration of different quantitative and qualitative influences. As a result the whole approach supports the collaborative identification of means for energy data acquisition and is applicable in several circumstances where energy data needs to be gathered efficiently. The decision procedures exemplary application has shown the general proficiency of the approach and that excessive data acquisition which contradicts efficiency is avoidable.

A reference decision model of first responders' decision-making

This paper introduces a generic decision model that connects three basic components in the response decision process: the stochastic information flow consisting of help requests and resource availabilities; situation awareness based on a spatiotemporal risk picture and response decisions allocating resources for response actions. The formalism utilised is the stochastic marked point process. The paper is written to support 'black-box' modelling of situation awareness in virtual training simulation, where a trainee's performance is compared with, e.g., the performance of experienced commanders with respect to key performance indicators (KPIs) obtained from such simulations. The paper also briefly discusses the use of the introduced decision model in developing agent-based models and simulations. It is also noted that the decision model encompasses the recognition-primed decision (RPD) model of naturalistic decision-making.

A reference decision model of first responders' decision-making

This paper introduces a generic decision model that connects three basic components in the response decision process: the stochastic information flow consisting of help requests and resource availabilities; situation awareness based on a spatiotemporal risk picture and response decisions allocating resources for response actions. The formalism utilised is the stochastic marked point process. The paper is written to support 'black-box' modelling of situation awareness in virtual training simulation, where a trainee's performance is compared with, e.g., the performance of experienced commanders with respect to key performance indicators (KPIs) obtained from such simulations. The paper also briefly discusses the use of the introduced decision model in developing agent-based models and simulations. It is also noted that the decision model encompasses the recognition-primed decision (RPD) model of naturalistic decision-making.

Classification images in a very general decision model

Most of the theory supporting our understanding of classification images relies on standard signal detection models and the use of normally distributed stimulus noise. Here I show that the most common methods of calculating classification images by averaging stimulus noise samples within stimulus-response classes of trials are much more general than has previously been demonstrated, and that they give unbiased estimates of an observer’s template for a wide range of decision rules and non-Gaussian stimulus noise distributions. These results are similar to findings on reverse correlation and related methods in the neurophysiology literature, but here I formulate them in terms that are tailored to signal detection analyses of visual tasks, in order to make them more accessible and useful to visual psychophysicists. I examine 2AFC and yes-no designs. These findings make it possible to use and interpret classification images in tasks where observers’ decision strategies may not conform to classic signal detection models such as the difference rule, and in tasks where the stimulus noise is non-Gaussian.

Multifactorial decision making based on type-2 fuzzy sets and factor space approach

In this paper, a new method for the representation of concept in factor space by using type-2 fuzzy sets, and the composition method of states of factors is developed. A general model of multifactorial decision making based on type-2 fuzzy sets is formulated, and the comprehensive evaluation proble m based on type-2 fuzzy sets is analyzed. Finally, two applied instances are given to demonstrate the effectiveness of the proposed methods.

Modeling the Effects of Attentional Cueing on Meditators

Training in meditation has been shown to affect functioning of several attentional subsystems, most prominently conflict monitoring, and to some extent orienting. These previous findings described the effects of cueing and manipulating stimulus congruency on response times and accuracies. However, changes in accuracy and response times can arise from several factors. Computational process models can be used to distinguish different factors underlying changes in accuracy and response times. When decomposed by means of the drift diffusion model, a general process model of decision making that has been widely used, both the congruency and cueing effects, is subserved by a change in decision thresholds. Meditators showed a modest overall increase in their decision threshold, which may reflect an ability to wait longer and collect more information before responding.

Precautionary paying for stochastic improvements under background risks

In a two-dimensional framework, we propose a general two-period decision model which extends the temporal precautionary saving and effort model. We relate the role of cross-prudence to the impact of background risks on paying for stochastic improvements of the future risk. We find that the effect of background risks introduced in the first period is consistent to signing cross derivatives of bivariate utility functions, which is independent of the type of stochastic improvements brought by additional paying; however, when the background risk occurs in the second period, that is not the case.

A framework for incorporating ecological releases in single reservoir operation

Most reservoir operation practices consider downstream environmental flow as a constraint to meet a minimum release. The resulting flow regime may not necessarily provide downstream aquatic conditions to support healthy ecosystems. These effects can be quantified in terms of changes in values of parameters that represent the flow regimes. Numerous studies have focused on determining the ecological response to hydrological alteration caused by reservoir operation. To mitigate hydrological alteration and restore the natural flow regime as much as possible, a reservoir operation framework is proposed to explicitly incorporate ecological flow requirements. A general optimization-based decision model is presented to consider simultaneously the multiple anthropogenic uses of the reservoir and desirable ecological releases represented by parameters that capture the flow regime. Multiple uses of the reservoir, including water supply, hydropower generation, etc., are modeled as a mixed integer programming problem. Hydropower generation, which is represented by a nonlinear function that usually depends on head and water flow, is linearized using a two-dimensional function. Investigations using a reservoir in Virginia, located in the southeastern United States, demonstrate that compared to standard releases based on current operation practice, releases simulated using this framework perform better in mimicking pre-development flows. The tradeoff between anthropogenic use and ecological releases is investigated. The framework is first demonstrated for instances with perfect stream flow information. To examine the flexibility of this framework in reservoir release management, monthly flow forecasts and disaggregated daily flow conditions are incorporated. Retrospective monthly flow forecasts are obtained through regression models that use gridded precipitation forecasts and gridded soil moisture estimates as predictors. A nonparametric method is chosen to disaggregate monthly flow forecasts to daily flow conditions. Compared with daily flow climatology, forecasted monthly and daily flow better preserves flow variability and result in lower changes of flow parameters under the proposed framework.

Inter-Temporal Optimal Asset Allocation and Time-Varying Risk Aversion

The inter-temporal optimal decision is related to investors risk preferenc e. In this study, we analyze the optimal asset allocation over investment horizon of invariable risk preference indicate d by constant risk aversion. To capture dynamic property of risk aversion, we relax the assumption of constant risk aversion and formulate a time-varying function in response to the impacts of time and wealth. Our general decision model built on time-varying risk aversion allows us to further investigate the inter-temporal optimal asset allocation. The numerical evidences from the model show that the optimal allocation of risky assets in portfolios is significantly related to investors risk aversion and that the time diversification is not exis ted under the time-varying risk aversion.

Sensor-based activity recognition using extended belief rule-based inference methodology.

The recently developed extended belief rule-based inference methodology (RIMER+) recognizes the need of modeling different types of information and uncertainty that usually coexist in real environments. A home setting with sensors located in different rooms and on different appliances can be considered as a particularly relevant example of such an environment, which brings a range of challenges for sensor-based activity recognition. Although RIMER+ has been designed as a generic decision model that could be applied in a wide range of situations, this paper discusses how this methodology can be adapted to recognize human activities using binary sensors within smart environments. The evaluation of RIMER+ against other state-of-the-art classifiers in terms of accuracy, efficiency and applicability was found to be significantly relevant, specially in situations of input data incompleteness, and it demonstrates the potential of this methodology and underpins the basis to develop further research on the topic.

A generic decision model for developing concentrated rural settlement in post-disaster reconstruction: a China study

The increasing impacts of natural disasters on disadvantaged rural areas, especially in developing countries, have led to concerns regarding post-disaster rural settlement reconstruction. Various approaches, including resettlement and in-situ reconstruction, have been adopted, both of which disregard changing the pattern of dispersed settlement in villages. Against a pattern of dispersed settlement, developing a concentrated rural settlement (CRS) within a village is argued to enable the resilience of rural villages and provide a basis for sustainable development after a disaster. Nevertheless, this has received little attention when determining a plan of actions for post-disaster reconstruction. No specific guidelines can be referenced when developing CRS in post-disaster reconstruction due to the lack of a generic decision model. Therefore, this study examines the process of developing CRS in post-disaster reconstruction by mapping four cases selected in Dujiangyan, China, after the 5.12 Sichuan Earthquake in 2008. The examination leads to establishing a generic decision model for developing CRS in post-disaster reconstruction, which incorporates all the proficiencies embodied in the existing practices. This model provides an alternative tool for planning CRS in post-disaster reconstruction. It can also serve as a vehicle for identifying both effective practices and weak areas by comparing varying cases.

A generic decision model of refueling policies: a case study of a Brazilian motor carrier

Considering the high impact of the transport on the total logistics cost, the transportation management is fundamental to maintain the company's competitiveness. Particularly in Brazil, fuel represents a significant cost for the motors carriers. So, this paper presents the development of a generic mathematic model that optimizes the fuel cost and assists the company's decision making on refuelling policy choices. Basically, in order to reduce the total cost, this model analyses the fuel prices variations in a road network and, thereby, it define: (i) which truck stop(s) to use, and (ii) how much fuel to buy at the chosen truck stop(s). Therefore, to assist the development of this model, a set of publications was raised related to the techniques of refuelling optimization. As opposed to the papers analyzed, in which, the models are validated using simulations, the presented model uses a case study as a reference and, in this case, the model provided a decrease of 2.3% in total fuel cost.