Mental Models

What are they?
They are cognitive frameworks which can help us interpret the world, make difficult decisions, and solve problems. They control how we perceive information, predict outcomes, and navigate the difficult complexity of the world by providing structured processes to evaluate information and knowledge. In Intelligence Analysis these models influence how analysts assess threats, identify patterns of behavior, and possibly anticipate what our adversaries might do next. While they can enhance our understanding and efficiency, they can also introduce biases if not challenged or redefined.
An effective analyst will employ multiple models to create a flexible model of thinking to enable dynamic adaptability in uncertain environments.

Examples –
Second-Order Thinking –
The practice of looking past the immediate consequences and anticipate longer second and third order effects that could be unintended based off of decisions. This approach is vital to understanding cascading effects in geopolitical strategies, economic policies, and military activity. Analysts who can apply this framework can better predict adversary responses, long-term risks, and unintended consequences which lead to a more strategic decision paradigm.

Occam’s Razor – A problem-solving principle that stipulates that the simplest explanation to a problem or situation, is usually the most accurate. This helps analysts avoid overcomplicating assessments (which happens all the time) and falling into the plethora of cognitive traps. While helpful, it must be applied with caution as intelligence situations can be very complex and sometimes this will not be the best approach to take.

Game Theory – Helps analysts anticipate how rational actors will behave when their choices depend on the actions of other players. In intelligence analysis we use it to asses adversary strategies, deterrence dynamics, and negotiation outcomes. The effectiveness of this model depends on how accurate we are with our assumptions pertaining to rationality, as real-world events might be influenced more by emotions, biases or simply incomplete information (more often than not).