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What are Sequential Decision Problems?

These are complex decision problems, in which conditions evolve over the course of time, where a person has the opportunity to make decisions periodically, and where each period’s decision affects future conditions. Many such problems also involve random or unpredictable events, meaning that the decision maker must consider the consequences of his or her decision, as well as those unknown factors, each time.

Sequential Decision Problems are discussed in more detail in the Guide to Recursive Models. You will observe that all of these problems depend on a time sequence, where information about relevant conditions are revealed over time, and where the manager, owner, or other subject person is capable of taking actions that affect the future course of events and future value of the business or other asset.


 

Examples of Sequential Decision Problems

Many of the most common, important, and difficult decision problems faced by businesses and people around the world can be structured as sequential decision problems. Some common examples, all of which could be solved using the Rapid Recursive® toolbox, include:

  • Credit Risk Decisions, such as financing decisions for loans that carry potential consequences
    Recursive Approach To Valuing Rental Properties

    Recursive Approach To Valuing Rental Properties

    for future business as well as repayment risk.

  • Real Options Valuation, including many management decisions where a company can begin to invest and then make changes in the future, or where counter-parties may do the same.
  • Investment Decisions, such as estimating the value of a risky investment, helping a venture capitalist decide whether to invest in an entrepreneurial firm, or understanding the intrinsic value of a publicly traded company’s stock.
  • Business Decisions, such as determining whether to invest in a new product or location, evaluating expansion opportunities, and considering long-term contracts.
  • Household Decisions, such as whether to return to graduate school or continue working; whether to pursue a career in a different city at risk of losing opportunities in another place; and whether to switch careers.
  • Retail Strategy Decisions, such as finding the most valuable marketing efforts for different customer segments, and determining optimal inventory ordering policies.
  • Oil and Gas Company Valuations, where such companies face significant uncertainty regarding volume of reserves, the price of oil, or country risks.
  • Natural Resource Management Decisions, such as mining extraction decisions.
  • Medical Decisions, such a s determining whether to discharge a patient, and deciding between different courses of treatment.
  • Intelligence and Military Decisions, such as searching for a moving target, and assessing certain country security-threats.

For more examples visit Application Packs. We also provide technical papers to demonstrate how some of the above problems have been solved by our software.

 

Our software utilizes breakthrough recursive methods to solve sequential decision problems. They have also been approached by some common methods such as discounted cash flows, decision trees, and Monte Carlo simulations that were not designed to handle such complex problems.

Visit Comparison to Traditional Methods for more information.

 

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