# Optimal Resource Procurement and the Price of Causality

## TL;DR

This paper introduces the concept of the price of causality in resource procurement, quantifies it, and proposes algorithms and mechanisms for fair cost allocation in uncertain demand scenarios.

## Contribution

It defines and analyzes the price of causality, providing bounds, exact calculations for special cases, and a fair cost allocation mechanism.

## Key findings

- Upper bound on the price of causality established
- Exact price computed for specific cases
- Proposed mechanism is fair and budget-balanced

## Abstract

This paper studies the problem of procuring diverse resources in a forward market to cover a set $\bf{E}$ of uncertain demand signals $\bf{e}$. We consider two scenarios: (a) $\bf{e}$ is revealed all at once by an oracle (b) $\bf{e}$ reveals itself causally. Each scenario induces an optimal procurement cost. The ratio between these two costs is defined as the {\em price of causality}. It captures the additional cost of not knowing the future values of the uncertain demand signal. We consider two application contexts: procuring energy reserves from a forward capacity market, and purchasing virtual machine instances from a cloud service. An upper bound on the price of causality is obtained, and the exact price of causality is computed for some special cases. The algorithmic basis for all these computations is set containment linear programming. A mechanism is proposed to allocate the procurement cost to consumers who in aggregate produce the demand signal. We show that the proposed cost allocation is fair, budget-balanced, and respects the cost-causation principle. The results are validated through numerical simulations.