page_title: Info-gap decision theory
section_title: Resource allocation
hierarchical_section_title: Info-gap decision theory / Example: resource allocation / Resource allocation
caption_attribution_description: Revenue per allocation
context_page_description: Info-gap decision theory is a non-probabilistic decision theory that seeks to optimize robustness to failure – or opportuneness for windfall – under severe uncertainty, in particular applying sensitivity analysis of the stability radius type to perturbations in the value of a given estimate of the parameter of interest. It has some connections with Wald's maximin model; some authors distinguish them, others consider them instances of the same principle.
  It has been developed since the 1980s by Yakov Ben-Haim, and has found many applications and described as a theory for decision-making under "severe uncertainty". It has been criticized as unsuited for this purpose, and alternatives proposed, including such classical approaches as robust optimization.
context_section_description: Suppose you are a project manager, supervising two teams: red team and blue team. Each of the teams will yield some revenue at the end of the year. This revenue depends on the investment in the team – higher investments will yield higher revenues. You have a limited amount of resources, and you wish to decide how to allocate these resources between the two groups, so that the total revenues of the project will be as high as possible.
  If you have an estimate of the correlation between the investment in the teams and their revenues, as illustrated in Figure 1, you can also estimate the total revenue as a function of the allocation. This is exemplified in Figure 2 – the left-hand side of the graph corresponds to allocating all resources to the red team, while the right-hand side of the graph corresponds to allocating all resources to the blue team. A simple optimization will reveal the optimal allocation – the allocation that, under your estimate of the revenue functions, will yield the highest revenue.
caption: Figure 2 – Revenue per allocation
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