Flexibility Planning in Decision Analysis

The theory of decision analysis is 'a systematic procedure for transforming opaque decision problems by a sequence of transparent steps' (Howard 1988 in Eisenführ & Weber 2003). The objective of the decision analysis is to produce rational decisions. According to Eisenführ &

Weber (2003), there are two requirements for rational decisions. First, procedural rationality like answering the appropriate problems, a suitable effort for decision-making, considering the uncertain future development and clarifying the preferences of the decision maker is required.

Second, the decision should be logically consistent. Goodwin & Wright (2004) or Eisenführ &

Weber (2003) offer an overview of the myriad literature regarding decision analysis.

A basic assumption of decision analysis is that a better and easier solution to complex decision problems is possible when the problem is divided into single components. The consideration of individual aspects reduces the complexity of the decision problem. Because of interactions between the aspects it is not possible to solve the decision problem in a single linear process;

instead, feedbacks between the aspects are required. According to Eisenführ & Weber (2003) decision analysis provides a general framework for decision-making. This framework is suitable for general decision analysis as well as for specific decisions about flexibility.

 Objectives and preferences of the decision maker: The objectives and preferences of the decision maker have to be described. The objectives influence the selection of alternative solutions and serve as benchmarks for effect modeling.

 Alternative solutions: In many decision situations reasonable alternative solutions are unknown, and it is part of the decision problem to generate such possible solutions. The final decision occurs by selecting the best alternative solution from the pool of possible alternative solutions.

 Environmental influences: The environmental influences, which are relevant for the decision, have to be modeled. Depending on the quality of information, the future prognosis is classified in certainty, risk and uncertainty.

 Consequences of each alternative solution and the environmental influence: The combined

effects of the alternative solutions on environmental influence are determined by effect modeling. The consequences of the decisions are then identified.

Decision analysis distinguishes different states of knowledge about future development. Besides a state of certainty, the deterministic knowledge of the future development risk and uncertainty are differentiated. 'Uncertainties are incalculable and not predictable developments.' (Buergin 1999). In uncertainty, possible future states, but not the probability of occurrence of these future states, are known. Contrastingly, the term risk includes the calculation of the probability of occurrence of events. Risk includes the aspect’s probability of occurrence and the extent of damage so that risk is defined as 'Risk = extent of damage x probability of occurrence' (Buergin 1999). Decision analysis has developed suitable decision rules for the different states of knowledge. For decisions under uncertainty only, the following limited decisions rules are permitted (Laux 2005; Scholles 2001):

 The minimax-principle is based on the pessimistic assumption which considers the most unfavorable results for the different possible future developments. The alternative solution with the lowest unfavorable results for all possible future developments is chosen. The other possible results are not considered (Laux 2005).

 The maximax-principle is based on the optimistic assumption, in which the most favorable results for all possible future developments are considered. The alternative solution with the highest possible maximum benefit for all future developments is chosen (Laux 2005).

 The minimax-regret-principle (also called ‘Savage-Niehans-Principle’) is a middle course between the minimax and the maximax principal. The alternative solutions are assessed not according to their immediate benefit; instead, the difference between the benefit of the assessed alternative and the maximal possible benefit of the other alternatives for different future developments is considered. An alternative is chosen which minimizes the disadvantage and regret for all possible future developments considered. Therefore, regret is the difference between the actual benefit and the benefit that would have been obtained if a different alternative solution had been chosen (Laux 2005).

 Probalistic-principle is an approach to cope with real uncertainties by means of converting the decision from a decision under real uncertainty to a decision under risk. Therefore, it is necessary to ascertain the probability of occurrence for the different possible future developments. If some expectations of future developments exist, then the probability of occurrence of these developments can be estimated. Otherwise, the Laplace-Principle can be used. According to the Laplace-Principle, all possible future developments have the same probability of occurrence. The principle is based on the assumption that if no probability of occurrence in known for any future developments, then there is no reason to expect different probabilities of occurrence for the future developments (Laux 2005).

These decisions rules for real uncertainty are relevant for flexibility for two reasons. On the one hand, the rules have to be considered for the assessment of flexibility for uncertain future alterations. And on the other hand, some of the principles can serve as basic rules to generate mechanisms of flexibility. An example of these principles being used to create flexibility in urban water management is described. So Katzenberger (2004) is using a 'flexible and no regret' principle, a combination of the 'minimized regret' principle and the approach of flexibility to cope with the uncertainties in relation to the impact of climate change.

As an additional method for decision making under uncertainty, the concept of 'flexible planning' contingency planning was developed. The approach is a mechanism to generate flexibility in

planning processes. In the following the approach of flexible planning is illustrated based on the description of Adam (1996). Comparable approaches are presented from Laux (2005) and Corsten & Gössinger (2005). Planning based on decision making on several and successive point of time. A basic problem of planning is according to Adam (1996) that a decision at one point of time creates commitments for the events to follow. Hence present decisions influence the basic for the decisions in future. A big capacity to act in future could only be developed when they are already considered beforehand in the first planning steps. In flexible planning the successive decisions are not considered as a single but as a multistage decision. To optimize the future capacity to act a contingency planning is developed and considered in the first decision. The decision alternative with the most promising contingency plan is chosen. According to Adam (1996) the present decisions should be decided in a way, which on the one hand does not limit the future capacity to act and on the other hand enables the reaction on possible future developments. A binding decision about the future decisions and the contingency planning is not chosen until better and certain information about the future development exist.

The practical application of the principal of flexible planning is restricted from Adam (1996).

Crucial point of every flexible planning is the question which extent of flexibility is required in the future. The required flexibility should be balances with the capability of change planned in the system. Nevertheless theoretical reflections from decision analysis suggest that the optimal level of flexibility in decisions under real uncertainty could not be determined ex ante, because the required information is missing (Adam 1996). Furthermore the variety of possible contingency plans (the numerous combinations of decision steps and different future states) causes an increasing effort for planning (Adam 1996; Kruschwitz 2005). Hence the method of flexible planning could not be used for extensive planning problems with several planning periods and decision nodes. According to Adam (1996) flexible planning is 'is more a thinking principle than a planning process which can recommended for practical planning problems'.

The advantages and disadvantages of decision analysis for the generation of flexibility are summarized:

 Decision analysis provides a systematic method for recognizing uncertainties and structuring the decision process. For flexibility in particular, the principle of contingency planning is relevant. Current decisions should be decided in such a way that there is considerable capability of change for later decisions. This includes the consideration of negative interactions for the capability for change, as well as an active development of capabilities for change. The principle of contingency planning offers a structure for the development of flexibility within the framework of planning and decision processes. A limitation is that decision analysis does not offer any assistance for the development of technical capabilities for change in physical systems.

 Decision analysis offers a systematic framework for decision-making. The relevance of objectives for rational decision-making is emphasized. The objectives are also required for the planning of flexibility; therefore, flexibility is related to the objectives of the system.

Furthermore, decision analysis offers basic approaches to cope with problems and conflicts in the planning process (e.g. deal with contradicting objectives when a system includes different goals or when there are conflicts between different decision makers). These problems are also relevant in the planning and generation of flexibility, which can include different objectives and/or several decision makers.

 According to de Neufville (2000), a major limitation of decision analysis is that approaches for the optimization of flexibility are missing. The most practical planning aspires to develop the best (optimal) concept, not just to select from the known semi optimal solutions. Hence a method for the measurement of flexibility in different planning alternatives as well as an approach to develop optimal alternative solutions is required.