Complexity Factors

Why do so many optimization efforts falter after a promising academic study or proof of concept? Their modeling and project plan are exposed as over-simplified when engaged with the messy reality of the business environment, systems and users. 

Princeton Consultants has identified eight complexity factors that often impede the progress from proof of concept to successful usage in production.  

The first four complexity factors can be thought of as external. The second four relate to how the optimization will be used by the organization.

These complexities are often combined with simultanous decision types across multiple asset classes.

Consider Princeton Consultants as “the glass is half full” types. We see complexities simply as barriers to jump over in order to succeed. After all, if it were easy, everyone else would have already done it.

Big Data

Extraordinarily large data sets or numbers of variables.

Noisy Data

Missing, conflicting or erroneous data.


Not certain, subject to probability.

Competitive Gaming

Competitors are acting on our actions.

Human in the Loop

A human decision maker is in the “loop” for each decision.

Black Box

The optimization has no human in the loop for each decision.


The problem is solved up front, but must be resolved with constantly updating information.


The optimization must give split-second responses.