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The shortcuts in unraveling complexity

Topic: Heuristics
by Hang, 2019 Cohort

Before talking about heuristics, I would like to invite you to think about the last time you played a board game that requires making moves. Could you foresee how the game would evolve and end? My guess would be maybe a few steps ahead but not the final outcome. Even the best players cannot see how the game will end when they make a move, because the possibilities grow exponentially as the game progresses. A simple game could even take the fastest computer on earth days to calculate. If we cannot guarantee a win when we make a certain move, the move is suboptimal. But a good player knows which the best move is to make because he or she evaluates the situation via putting knowledge, experience, and judgement together in a unique way before coming to a decision. This decision-making process, often referred to as “cognitive shortcut”, is heuristic. A heuristic is an approach to problem solving which adopts a practical method. It is fast and efficient, in that it effectively reduces days of computing down to a few seconds of judgment, but imperfect and suboptimal, in that the best outcome is not guaranteed.

Heuristics are wired in our brain in the complex process of evolution and have helped our decision- making process every day. Practitioners have studied heuristic reasoning and equipped heuristics with a significant amount of computation power to unravel the complexity in problems that are large in scale and intricate in nature. Heuristics are theorized into different categories. The two most commonly known types are availability heuristic and representativeness

1. Availability heuristic draws from memory and enables us to compare the current situation with similar scenarios we have experienced before. Computer vision techniques apply this reasoning by training models with a significant amount of data to help machines recognise objects in the environment which mimics how human vision and brain function.
2. Representativeness heuristic draws prototypes from our cognition and matches the current scenario into these mental prototypes. Ray Dalio, founder of the most successful hedge fund Bridgewater Associates, explained in his book principles how he had studied the historical debt crises and investigated the cause-effect relationships underneath. He incorporated factors that could lead to a debt crisis into a prototype, which is essentially a powerful representativeness heuristic for understanding debt crises, and successfully predicted and navigated around the 2008 financial crisis.

From the above examples, we can see that heuristic thinking is very powerful in decision-making process and unraveling complexities. But we do need to accept its imperfection. The best chess player did not win every round, the best camera cannot recognise objects 100% accurately although close and Ray Dalio didn’t profit on every single investment decision.

In conclusion, heuristic thinking is practical and efficient in unraveling complexity. But they are subject to the model builders’ cognition of the complexity underneath, therefore biased and suboptimal. It could be used as an approximation of the best solution given limited computation power but should be avoided in areas that require absolute precision.

Explore this topic further#

• Wikipedia page{.link-ext target=”_blank”}
• Amos Tversky and Daniel Kahneman, Judgement under uncertainty: Heuristics & Biases{.link-ext target=”_blank”}
• Heuristics and biases in decision making, explained{.link-ext target=”_blank”} (YouTube)

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Disclaimer#

This content has been contributed by a student as part of a learning activity.
If there are inaccuracies, or opportunities for significant improvement on this topic, feedback is welcome on how to improve the resource.
You can improve articles on this topic as a student in "Unravelling Complexity", or by including the amendments in an email to: Chris.Browne@anu.edu.au

Before talking about heuristics, I would like to invite you to think about the last time you played a board game that requires making moves. Could you foresee how the game would evolve and end? My guess would be maybe a few steps ahead but not the final outcome. Even the best players cannot see how the game will end when they make a move, because the possibilities grow exponentially as the game progresses. A simple game could even take the fastest computer on earth days to calculate. If we cannot guarantee a win when we make a certain move, the move is suboptimal. But a good player knows which the best move is to make because he or she evaluates the situation via putting knowledge, experience, and judgement together in a unique way before coming to a decision. This decision-making process, often referred to as “cognitive shortcut”, is heuristic. A heuristic is an approach to problem solving which adopts a practical method. It is fast and efficient, in that it effectively reduces days of computing down to a few seconds of judgment, but imperfect and suboptimal, in that the best outcome is not guaranteed.

Heuristics are wired in our brain in the complex process of evolution and have helped our decision- making process every day. Practitioners have studied heuristic reasoning and equipped heuristics with a significant amount of computation power to unravel the complexity in problems that are large in scale and intricate in nature. Heuristics are theorized into different categories. The two most commonly known types are availability heuristic and representativeness

1. Availability heuristic draws from memory and enables us to compare the current situation with similar scenarios we have experienced before. Computer vision techniques apply this reasoning by training models with a significant amount of data to help machines recognise objects in the environment which mimics how human vision and brain function.
2. Representativeness heuristic draws prototypes from our cognition and matches the current scenario into these mental prototypes. Ray Dalio, founder of the most successful hedge fund Bridgewater Associates, explained in his book principles how he had studied the historical debt crises and investigated the cause-effect relationships underneath. He incorporated factors that could lead to a debt crisis into a prototype, which is essentially a powerful representativeness heuristic for understanding debt crises, and successfully predicted and navigated around the 2008 financial crisis.

From the above examples, we can see that heuristic thinking is very powerful in decision-making process and unraveling complexities. But we do need to accept its imperfection. The best chess player did not win every round, the best camera cannot recognise objects 100% accurately although close and Ray Dalio didn’t profit on every single investment decision.

In conclusion, heuristic thinking is practical and efficient in unraveling complexity. But they are subject to the model builders’ cognition of the complexity underneath, therefore biased and suboptimal. It could be used as an approximation of the best solution given limited computation power but should be avoided in areas that require absolute precision.