INFO-GAP DECISION THEORY FOR DESIGN AND PLANNING 
                                OR: 
                    WHY `GOOD' IS PREFERABLE TO `BEST' 

                        Prof. Yakov Ben-Haim 
          Yitzhak Moda'i Chair in Technology and Economics   
            Technion - Israel Institute of Technology           
                Visiting Professor at Georgia Tech 
                
                             Venue:
                    RAND Corporation
                               Santa Monica, CA
                        10:00, Friday, 21 February 2003

Good performance is better than poor performance, but the need for feasibility must temper the
aspiration for high performance. To achieve this balance we must model and manage our severely deficient information about, and understanding of, the processes we confront. We must also re-evaluate our concepts of rational decision-making. This seminar focusses on three central ideas. 

1. Severely deficient information and understanding can only be quantified by a highly unstructured model of uncertainty. For this we employ info-gap models rather than probability. Info-gap models are set-theoretic representations of uncertainty which employ no distribution functions. Info-gap models are axiomatically utterly different from both probability and fuzzy
logic, since info-gap models focus on the set-structure of uncertainty rather than on measure-theoretical representations. Info-gap models are particularly suited to representing 
sparse information since they make no assertions about frequencies of, or beliefs about, rare events.

2. In strategic planning or conceptual design, there is an irrevocable trade-off between high 
performance and high immunity-to-uncertainty. We illustrate this with two heuristic examples. The first is the up-dating of a mathematical system-model based on data. The second is the design of a strategy for environmental management subject to uncertainty about the underlying processes. We will touch on two general theorems which underlie these examples. 

3. Uncertainty may be either pernicious or propitious, and in designing under uncertainty we 
should protect against adversity while also enabling the exploitation of opportunities. To do 
this, we will discuss two info-gap immunity functions. The robustness function underlies a 
decision strategy which satisfices performance and maximizes immunity to failure. The 
opportunity function supports decisions which "windfall" the performance and minimize the 
immunity to opportunity. We will touch on the trade-offs and trade-ons which may arise between 
these strategies.