scholarly journals Service Innovation Decision Analysis Based on Influence Diagrams

2018 ◽  
Vol 13 (5) ◽  
pp. 895-907
Author(s):  
Liang Tan
Keyword(s):  
Decision Making ◽  
Decision Analysis ◽  
Directed Graph ◽  
Probabilistic Model ◽  
Service Innovation ◽  
The Other ◽  
Decision Problems ◽  
Influence Diagrams ◽  
Influence Diagram ◽  
Interactive Dynamic

The influence diagram is a probabilistic model for presenting decision problems as a directed graph. In this study, the dynamic influence diagram and the interactive dynamic influence diagram are used to model the three parties to service innovation: customers, suppliers, and service enterprises. The models analyze the decisions of these dierent parties and describe the process by which service enterprises should consider their own innovation conditions as well as those of the other parties, that is, customers and suppliers. Moreover, during the process of service innovation, service enterprises should be in constant communication with customers and suppliers. After the customers and suppliers respond, service enterprises can modify their innovation decision-making, and improve service innovation quality and income.

Decision Analysis for Small Business

1983 ◽  
Vol 8 (2) ◽  
pp. 15-26 ◽  
Author(s):  
David F. Dianich ◽  
Jatinder N. D. Gupta
Keyword(s):  
Decision Making ◽  
Problem Solving ◽  
Small Business ◽  
Decision Analysis ◽  
Decision Problems ◽  
Managerial Decision ◽  
Managerial Decision Making ◽  
Information Base ◽  
Business Executives ◽  
Intuitive Judgments

Decision analysis is a relatively recent subject to enter the field of managerial decision-making. The application of decision analysis to managerial decision problems is useful especially in situations where the problem is complex, outcomes are risky, and intuitive judgments are rather difficult. This paper discusses the fundamental concepts of decision analysis and illustrates its utility to small business executives in analyzing and resolving their problems more effectively. It is shown that the decision analysis approach is an extension of the executive's natural problem solving style—that of analyzing contingencies—and can increase the information base useful in making better intuitive decisions.

scholarly journals Distribution Network Risk Assessment Using Multicriteria Fuzzy Influence Diagram

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Aleksandar Janjic
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Distribution Network ◽  
Circuit Breaker ◽  
Influence Diagrams ◽  
Influence Diagram ◽  
Planning Decision ◽  
Fuzzy Probabilities ◽  
Operational Issues ◽  
The Impact

Risk assessment of distribution assets is one of the most important factors in the process of network development or maintenance planning decision-making. The process of decision-making is faced with uncertainties, involving technical, financial, safety, environmental, and other operational issues that make standard risk assessment techniques insufficient. Probabilistic uncertainties require appropriate mathematical modeling and quantification when predicting future state of the nature or the value of certain parameters. The paper is proposing a new methodology for the multicriteria risk assessment of the distribution network assets, based on influence diagrams and fuzzy probabilities. Influence diagram has been used to determine all relevant factors concerning risks and their interdependencies are depicted. Fuzzy probabilities are represented by triangular fuzzy numbers with constraints on feasibility of elicited probabilities. This methodology enables the decision process in uncertain environment, with the impact evaluation of each particular distribution asset, or the asset component. The methodology is illustrated on the example of a distribution substation circuit breaker maintenance strategy selection.

scholarly journals Toward data-driven solutions to interactive dynamic influence diagrams

2021 ◽  
Author(s):  
Yinghui Pan ◽  
Jing Tang ◽  
Biyang Ma ◽  
Yifeng Zeng ◽  
Zhong Ming
Keyword(s):  
Decision Making ◽  
Domain Knowledge ◽  
Strong Support ◽  
Study Data ◽  
Data Driven ◽  
Influence Diagrams ◽  
Sequential Decision ◽  
Data Driven Decision Making ◽  
Data Driven Approach ◽  
Interactive Dynamic

AbstractWith the availability of significant amount of data, data-driven decision making becomes an alternative way for solving complex multiagent decision problems. Instead of using domain knowledge to explicitly build decision models, the data-driven approach learns decisions (probably optimal ones) from available data. This removes the knowledge bottleneck in the traditional knowledge-driven decision making, which requires a strong support from domain experts. In this paper, we study data-driven decision making in the context of interactive dynamic influence diagrams (I-DIDs)—a general framework for multiagent sequential decision making under uncertainty. We propose a data-driven framework to solve the I-DIDs model and focus on learning the behavior of other agents in problem domains. The challenge is on learning a complete policy tree that will be embedded in the I-DIDs models due to limited data. We propose two new methods to develop complete policy trees for the other agents in the I-DIDs. The first method uses a simple clustering process, while the second one employs sophisticated statistical checks. We analyze the proposed algorithms in a theoretical way and experiment them over two problem domains.

scholarly journals Matching Revenues and Costs: The Counter-Intuitive Rationality of Direct Costing

2019 ◽  
Vol 15 (1) ◽  
pp. 202
Author(s):  
Anna Maria Moisello ◽  
Piero Mella
Keyword(s):  
Decision Making ◽  
Production Capacity ◽  
The Other ◽  
Decision Problems ◽  
Optimal Production ◽  
Numerical Evidence ◽  
Lead Management ◽  
Simple Sets ◽  
Full Absorption

This study investigates the consequences of adopting two simple sets of rules the manager can consider as perfectly rational and follow in his decisions regarding price, volume and mix of the various products. The first set follows the full (absorption) costing method logic, while the second is based on the direct (variable, marginal) costing method logic. It shows that costing systems adopting the full-costing method can lead management to make non-rational decisions regarding the setting of prices, acceptance of orders, make or buy choices and, above all, determination of the optimal production mix through programming and budgeting. On the other hand, using the direct costing method allows the manager to achieve rational results during the decision-making and planning phases, even if these often appear counter-intuitive when compared with the results achieved using the full costing method, which seem to conform to naïve intuition. The risk in the latter case is even more serious when we are dealing with multi-production firms operating under conditions of limited production capacity regarding one or more factors, as occurs most of the time. The demonstration of the thesis of the superiority of direct costing method rules in management decisions related to the problem of the matching costs and revenues is carried out with numerical evidence, formulating a set of decision problems that are solved by comparing the results obtained both with the full costing method rules and with the direct costing method rules.

scholarly journals Decision theory with multiple criteria: an aplication of ELECTRE IV and TODIM to SEBRAE/RJ

2009 ◽  
Vol 29 (3) ◽  
pp. 577-590 ◽  
Author(s):  
Luís Alberto Duncan Rangel ◽  
Luiz Flávio Autran Monteiro Gomes ◽  
Rogério Amadel Moreira
Keyword(s):  
Decision Making ◽  
Decision Analysis ◽  
Prospect Theory ◽  
Practical Experience ◽  
Social Consequences ◽  
The Other ◽  
Decision Making Process ◽  
French School ◽  
Criteria Weights ◽  
Janeiro State

This paper presents an application of two methods of Multi-Criteria Decision Analysis (MCDA), ELECTRE IV and TODIM, in order to tackle the problem of ranking projects with important economic and social consequences in the Rio de Janeiro State. These projects are ranked under the presence of quantitative and qualitative criteria. ELECTRE IV is a method of the French School of MCDA whose use does not rely on knowledge of criteria weights. TODIM, on the other hand, is a method that is based on the paradigm of Prospect Theory and that has elements of both the French and the American School of MCDA. Although ranks obtained by both methods were different, the same project was ranked as the best alternative according in both. The practical experience described in this paper has suggested that the use of MCDA methods in the ranking of project can significantly clarify the decision making process.

scholarly journals Exploiting Model Equivalences for Solving Interactive Dynamic Influence Diagrams

2012 ◽  
Vol 43 ◽  
pp. 211-255 ◽  
Author(s):  
Y. Zeng ◽  
P. Doshi
Keyword(s):  
Graphical Models ◽  
Exponential Growth ◽  
Model Space ◽  
Influence Diagrams ◽  
Influence Diagram ◽  
Time Step ◽  
Large Space ◽  
Equivalent Models ◽  
Conflicting Objectives ◽  
Interactive Dynamic

We focus on the problem of sequential decision making in partially observable environments shared with other agents of uncertain types having similar or conflicting objectives. This problem has been previously formalized by multiple frameworks one of which is the interactive dynamic influence diagram (I-DID), which generalizes the well-known influence diagram to the multiagent setting. I-DIDs are graphical models and may be used to compute the policy of an agent given its belief over the physical state and others' models, which changes as the agent acts and observes in the multiagent setting. As we may expect, solving I-DIDs is computationally hard. This is predominantly due to the large space of candidate models ascribed to the other agents and its exponential growth over time. We present two methods for reducing the size of the model space and stemming its exponential growth. Both these methods involve aggregating individual models into equivalence classes. Our first method groups together behaviorally equivalent models and selects only those models for updating which will result in predictive behaviors that are distinct from others in the updated model space. The second method further compacts the model space by focusing on portions of the behavioral predictions. Specifically, we cluster actionally equivalent models that prescribe identical actions at a single time step. Exactly identifying the equivalences would require us to solve all models in the initial set. We avoid this by selectively solving some of the models, thereby introducing an approximation. We discuss the error introduced by the approximation, and empirically demonstrate the improved efficiency in solving I-DIDs due to the equivalences.

scholarly journals Delegation to a Group

2020 ◽  
Author(s):  
Sebastian Fehrler ◽  
Moritz Janas
Keyword(s):  
Decision Making ◽  
Decision Analysis ◽  
Information Acquisition ◽  
Information Aggregation ◽  
The Other ◽  
Trade Off ◽  
Individual Level ◽  
The One ◽  
The Individual ◽  
The Cost

We study the choice of a principal to either delegate a decision to a group of careerist experts or to consult them individually and keep the decision-making power. Our model predicts a trade-off between information acquisition and information aggregation. On the one hand, the expected benefit from being informed is larger in case the experts are consulted individually. Hence, the experts either acquire the same or a larger amount of information, depending on the cost of information, than in case of delegation. On the other hand, any acquired information is better aggregated in the case of delegation, in which experts can deliberate secretly. To test the model’s key predictions, we run an experiment. The results from the laboratory confirm the predicted trade-off despite some deviations from theory on the individual level. This paper was accepted by Yan Chen, decision analysis.

Decomposable Probabilistic Influence Diagrams

1991 ◽  
Vol 5 (2) ◽  
pp. 229-243 ◽  
Author(s):  
C. C. Chyu
Keyword(s):  
Stochastic Modeling ◽  
Directed Graph ◽  
Efficient Algorithm ◽  
Conditional Independence ◽  
Directed Graphs ◽  
Influence Diagrams ◽  
Influence Diagram ◽  
Modeling Tool ◽  
Graph Theoretic ◽  
Independence Properties

Probabilistic influence diagrams are a useful stochastic modeling tool. To calculate probabilities of interest relative to a probabilistic influence diagram efficiently, it will be helpful for us to use an associated decomposable-directed graph. We first explore and discuss some graph-theoretic and conditional independence properties of decomposable probabilistic influence diagrams. These properties are helpful in providing an efficient algorithm for obtaining a posterior decomposable probabilistic influence diagram given the state of one or more observed nodes. The connection between Shachter's “sequential creation of conditionally barren nodes” concept and Lauritzen and Spiegeihalter's “moralization and triangulation” algorithm for calculating probabilities relative to a probabilistic influence diagram is made explicit. We also discuss how to use wisely the concepts of “sequential creation of conditionally barren nodes” and “merging nodes” together with the graph-theoretic properties of decomposable directed graphs to compute probabilities relative to probabilistic influence diagrams.

REPRESENTING AND SOLVING ASYMMETRIC DECISION PROBLEMS

2003 ◽  
Vol 02 (02) ◽  
pp. 217-263 ◽  
Author(s):  
THOMAS D. NIELSEN ◽  
FINN V. JENSEN
Keyword(s):  
Decision Problem ◽  
Evaluation Methods ◽  
Decision Problems ◽  
Influence Diagrams ◽  
Influence Diagram ◽  
Bayesian Decision ◽  
Formal Framework ◽  
Qualitative Level ◽  
Efficient Representation

This paper deals with the representation and solution of asymmetric Bayesian decision problems. We present a formal framework, termed asymmetric influence diagrams. The framework is based on the syntax and semantics of the traditional influence diagram, and allows an efficient representation of asymmetric decision problems. As opposed to existing frameworks, the asymmetric influence diagram primarily encodes asymmetry at the qualitative level and it can therefore be read directly from the model. We give an algorithm for solving asymmetric influence diagrams. The algorithm initially decomposes the asymmetric decision problem into a structure of symmetric subproblems organized as a tree. A solution to the decision problem can then be found by propagating from the leaves towards the root using existing evaluation methods to solve the subproblems.

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