The Exploitation-Exploration dilemma’s fundamental role in intelligence and our lives
The Exploitation-Exploration dilemma is deeply relevant, not only to business strategy, but to many aspects of our lives.
It is also a fundamental underpinning of intelligence, human and artificial.
The dilemma is essentially: should we continue with what we have, or leave that behind to find something better?
Explore-exploit’s role in intelligence
The exploitation-exploration dilemma lies at the very roots of the evolution of intelligent life.
The first lifeforms that could move had a choice: whether to move and in which direction. Their brains were completely focused on this fundamental choice between options.
Dopamine-driven foraging strategies evolved in early species to optimize between existing and potential food sources.
These structures still drive human behaviour today, including in how we consume information, as I explore in Chapter 3 of Thriving on Overload.
The increasing value of openness to experience
Another critical point is the personality dimension of ‘Openness to Experience’, which is essentially our propensity to seek or be open to new possibilities.
As the pace of change increases, the value of openness rises.
In Chapter 5 of Thriving on Overload I examine how we can usefully increase our openness to experience and ideas.
This allows us explore, discover, and benefit from more of the growing array of options and possibilities available to us.
Explore-exploit in life and business
Once you think about it, the exploit-explore dilemma plays a central role in almost all aspects of life and business. Here is a small handful of examples:
Innovation strategy: Innovation is about renewal: evolving your business as the world changes. How do you allocate limited resources between supporting established business models and building new unproven opportunities?
Customer relationships: Do you focus on creating value with your existing customers, or allocate more resources to finding new customers or addressing new market segments?
Career choices: Do you continue in a job that is rewarding and safe despite its drawbacks, or seek new opportunities that involve uncertainty and high degrees of challenge?
Holiday choices: Do you keep going back to places that you know well and love, or try new adventures that might offer new possibilites but could also disappoint?
Relationships: Long-term relationships require investment and offer security but go through ups and downs; at what point do you leave for the deep uncertainty of being single but also the possibility of something better?
Explore-exploit in AI
The exploit-explore dilemma has been central to many aspects of the development of artificial intelligence, often by modelling our understanding of human intelligence. Here are a few examples:
Reinforcement learning: This fundamental approach to AI was conceived from studying human exploit-explore strategies in human learning. It was originally laid out by Richard Sutton and Andrew Barto in their seminal1998 book Reinforcement Learning.
Attention mechanisms: Attention mechanisms in AI balance exploring new patterns and exploiting known ones. Transformer models, which underlie modern generative AI, efficiently distribute attention across tokens by focusing on the most relevant data.
Evolutionary algorithms: A fundamental paramter is the degree of evolution across generations, varying from only selecting the best-performing entities to incorporating mutations that do not offer evident advantaage.
Generative Adversarial Networks: The generator can focus on very slight variations to existing outputs that have fooled the discriminator, or try more dramatic differences that could lead to lower performance but may uncover more promising avenues.
Training data selection: Existing validated data sets provide consistent quality outputs. Expanding training data to include lower quality sources can increase breadth of competence but could make spurious or incorrect outputs more likely.
Explore more than exploit
Whenever you are considering whether to stay with what you have or to try something new, you are expressing a deep facet of your intelligence.
But as the pace of change gets faster, and these strategies are incorporated into AI, the balance of value is shifting ever-more to exploring.
Apply your intelligence to these fundamental choices, while tending more to exploration.
