The Difference | Scott E. Page

Summary of: The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies – New Edition
By: Scott E. Page


Embark on a fascinating exploration into the power of diversity with Scott E. Page’s ‘The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies.’ Discover how cognitive diversity, a concept consisting of diverse perspectives, interpretations, heuristics, and predictive models, can drastically improve problem-solving and decision-making in groups. The book dives into a myriad of examples, from World War II codebreakers to prediction markets, illustrating the edge that diverse groups possess over homogenous ones. Learn about the challenges and advantages of diversity, and how organizations can leverage this magic ingredient for their betterment.

Cognitive Diversity

During World War II, the British cracked the Nazi’s “Enigma” code system using a group of diverse experts that included linguists, crossword puzzle-masters, philosophers, historians, and even classicists. This remarkable feat demonstrates that diverse groups can often solve problems more efficiently than homogeneous groups. Cognitive diversity, which is diversity in cognitive tools, is key to achieving this result. Different people have various tools that come from sources like experience, education, intelligence, ethnicity, gender, and age. These tools fall into the four main types of cognitive diversity: perspectives, interpretations, heuristics, and predictive models. By using diverse perspectives, interpretations, and heuristics, a person or group increases their likelihood of finding a solution. Predictive models help move from the known to the unknown. Homogeneous teams often overlook cognitive diversity’s benefits, leading to underperformance. Ultimately, identity diverse teams, cities, and societies can perform better, but they often fail to do so.

Diversity is the Key

A diverse group of problem solvers is more likely to find the best solution compared to a homogeneous group. Computer simulations showed that given certain conditions, diversity beats conformity. The researchers represented the set of solutions to a problem topologically using a three-dimensional surface. The “best” solution was the highest peak. Two groups of agents were set loose on this landscape, one with homogeneous agents and the other with diverse agents. The diverse group found the best solution while the homogeneous group got stuck in the foothills. The key conditions for the diverse group to find the best solution were that the problem had to be difficult, the solvers had to be relatively smart, some subset of the diverse group had to be able to spot a better solution, and the diverse collection had to be fairly big and drawn from a large population. The diverse predictive model yields similarly good results, proven by the success of prediction markets such as the Hollywood Stock Exchange, the Iowa Electronic Markets, and Crowds contain multitudes, and their members have diverse predictive models. This diversity allows them to aggregate dispersed information and use diverse perspectives and interpretations to “lump” reality differently, often beating polls or expert opinions.

Diverse Preferences in Decision Making

The concept of diverse preferences is not always advantageous, especially when it comes to making decisions. This is because diverse preparedness sometimes leads to breakthroughs, but when diverse preferences need to be aggregated, challenges arise. In most cases, voting is used to aggregate these preferences, but it has four significant flaws. Firstly, when individual preferences are too heterogeneous, they fail to aggregate. Secondly, voting processes sometimes produce arbitrary decisions that suit no one. Thirdly, voters can manipulate the voting system, which can determine the outcome. Lastly, voting systems may underprovide public goods that benefit everyone. This occurs when a group with diversified preferences cannot agree on the type of public goods it wants, leaving everyone worse off. Although instrumental preference aggregation problems can be resolved, fundamental preference aggregation issues are challenging to solve. Nonetheless, we can introduce tools to combat the difficulties faced with diverse preferences.

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