Using Stochastic Optimization and Real-Options Models to Value Private Sector Incentives to Invest in Food Protection Measures

Abstract

Agro-terrorism has become a major concern since the September 11, 2001, terrorist attacks due to characteristics that create unique problems for managing the threat of an agro-terrorist attack. The costs of trucking delays alone were in the tens of millions of dollars. Over the last few years, the government has spent billions of dollars on biological surveillance and record keeping in preventing potential attacks. Several public and private initiatives are currently in use. Examples include 1) the bio-terrorism regulation of 2004 on maintenance of records; 2) establishment of food protection centers for research and teaching excellence; and 3) investments in emerging technology, such as radio frequency monitoring (RFEM) technology, with the potential to track shipments and provide real-time data that can be used to prevent agro-terrorism risks along food supply chains. This thesis addresses the costs and risk premiums associated with alternative tracking strategies, where and when along the milk supply chain these strategies will reduce the most risks, and what policy implications are associated with the most costeffective tracking strategy. To accomplish these objectives, stochastic optimization is used to determine the costs and risk premiums of alternative tracking strategies. Next, the realoptions method along with a portfolio of options, also referred to as the "tomato garden" framework, is used to determine where and when alternative intervention strategies should be implemented to reduce the most risks. Finally, policy implications are derived on the cost-risk tradeoffs, probability of attacks, and containment efforts if there is an attack by using game theory to determine the incentives needed to motivate participants in the milk supply chain to invest in security measures.