Yakov Ben-Haim, 2010, Info-Gap Economics: An Operational Introduction, Palgrave. Description and endorsements.
Chapter 1: Info-Gap Theory in Plain English
Chapter 2: A First Look: Stylized Example
2.1 Problem Formulation
Michael Ben-Gad, Yakov Ben-Haim and Dan Peled, 2019, Allocating security expenditures under Knightian uncertainty: An info-gap approach, Defence and Peace Economics, to appear. Abstract. Free copies while they lasthere.
Yakov Ben-Haim and Maria Demertzis, 2016, Decision making in times of uncertainty: An info-gap perspective, Economics, The Open-Access, Open Assessment e-journal. On-line version.
Yakov Ben-Haim, 2008, Info-gap economics: An overview. Presented in part at the Research Forum on “Risk and Uncertainty in Monetary Policy”, Centre for Central Bank Studies, Bank of England, London, 9-11 June 2008. pdf preprint.
Tools and concepts of optimization are widespread in decision-making, design and planning. There is a moral imperative to ‘do our best’. Optimization underlies theories in physics and biology, and economic theories often presume that economic agents are optimizers. We argue that, in decisions under uncertainty, what should be optimized is robustness rather than performance. We discuss the equity premium puzzle from financial economics, and explain that the puzzle can be resolved by using the strategy of satisficing rather than optimizing. We discuss design of critical technological infrastructure, showing that satisficing of performance requirements – rather than optimizing them – is a preferable design concept. We explore the need for disaster recovery capability and its methodological dilemma. The disparate domains – economics and engineering – illuminate different aspects of the challenge of uncertainty and of the significance of robust-satisficing.
Yakov Ben-Haim, 2011, When is non-probabilistic robustness a good probabilistic bet? Working paper.
Yakov Ben-Haim, 2014, Robust satisficing and the probability of survival, Intl. J. of System Science, 45(1): 3-19.
Vanessa M. Adams and Robert L. Pressey, 2011, An info-gap model to examine the robustness of cost-efficient budget allocations, ICVRAM 2011: 1st International Conference on Vulnerability and Risk Assessment and Management, April 11-13, 2011, University of Maryland, College Park, pp.971-979.
Benefit cost ratios (BCR) have been applied to conservation decisions for two reasons: cost-efficiency and transparency in decision making. Because BCRs are ratios of benefits to costs, the uncertainties associated with the two components (benefits and costs) are compounded. Therefore, BCRs can potentially involve more uncertainty than allocation strategies based solely on maximizing benefits. The robustness of decisions, defined here as the inverse of the number of misallocations due to uncertainties in benefits and costs of projects, is an unexplored component of applying BCRs to conservation decision making. To investigate the robustness to uncertainty of conservation investment with BCRs, we developed an information-gap model (info-gap) for using BCRs in selecting “portfolios” of conservation projects. Our model allows us to explore how uncertain we can be in our estimates of benefit and cost parameters while still selecting a portfolio that performs better than a critical threshold of misallocations perceived to be unacceptable. We first give a full theoretical description of our info-gap model formulation and then explore applications of the model to several hypothetical data sets.
Lior Davidovitch and Yakov Ben-Haim, 2011, Robust resource allocation: An info-gap approach, ICVRAM 2011: 1st International Conference on Vulnerability and Risk Assessment and Management, April 11-13, 2011, University of Maryland, College Park, pp.988-995.
A decision maker wishes to distribute a fixed amount of resources between several alternatives. Each alternative responds differently to allocation, as expressed by its responsiveness. The alternatives’ different responsiveness to investment can be exploited for efficient allocation of resources. However, the responsiveness is highly uncertain, so prediction is difficult and uncertainty must be accounted for in designing an allocation. We use info-gap theory for satisficing (not minimizing) accumulated loss from several alternatives. We demonstrate the trade-off between robustness to uncertainty and total loss, and show the tight connection between the decision maker’s notion of adequacy and the resulting allocation.