A New Indeterminacy Decision Making Approach Considering Partial Environmental State Knowledge Bo FENG a,b , Zhipeng HUI a , Junwen FENG a,b,1 a Nanjing University of Science and Technology, 210094, China b Nanjing Audit University Jinshen College, 210023, China Abstract. The types of decisions people make depend on how much knowledge or information they have about the decision environmental situation or called state. There are three decision-making environments: decision making under certainty, decision making under uncertainty and decision making under risk. Based on possibility theory and evidence theory, a new indeterminacy decision making approach with the decision-maker’s environmental knowledge information about the state of nature is proposed. This approach provides a general framework for three types of decision problems, that is, deterministic, risky and uncertain problems. Keywords. Decision analysis, Possibility theory, Evidence theory, Fuzzy analysis 1. Introduction Decision analysis can be used to develop an optimal strategy when a decision maker is faced with several decision alternatives and an uncertain or risk-filled pattern of future environmental state events. Even when a careful decision analysis has been conducted, the uncertain future events make the final consequence uncertain [1]. In some cases, the selected decision alternative may provide good or excellent results. In other cases, a relative unlikely future event may occur, causing the selected decision alternative to provide only fair or even poor results [2]. We begin the study of decision analysis by considering problems that involve reasonably few decision alternatives and reasonably few possible future environmental state events [3]. The payoff tables are introduced to provide a structure for the decision problem and to illustrate the fundamentals of decision analysis [4]. Consider the decision making problem expressed by Table 1: Table 1 Decision making problem Alternatives n S S S ⋯ 2 1 m A A A ⋮ 2 1 mn m m n n Q Q Q Q Q Q Q Q Q ⋯ ⋮ ⋯ ⋮ ⋮ ⋯ ⋯ 2 1 2 22 21 1 12 11 1 Corresponding Author, Nanjing University of Science and Technology, Nanjing, China; E-mail: 313472714@qq.com Fuzzy Systems and Data Mining VII A.J. Tallón-Ballesteros (Ed.) © 2021 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). doi:10.3233/FAIA210221 469