Diagram: Fitness Peaks

Fitness Peaksdefinition.php

Fitness ‘peaks’ are regimes wherein a given agent behavior maximizes energetic returns while minimizing outputs. Peaks are thus optimum behaviors in phase space - though there may be numerous peaks, each employing different strategies. See also Fitness Landscape

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A fitness peak represents a regime of behavior within a system that once, arrived upon, cannot be moved smoothly away from without reducing fitness.

Example: Imagine that an agent can vary its behavior according to three different Variables. Perhaps the agent is an adjustable bed, embedded with sensors, and 'fitness' for the bed pertains to how little movement it detects from a sleeper. This 'smart'  bed can adjust its temperature, its firmness, and the volume of soothing music it emits (the extent of adjustment possible are the bed's Degrees of Freedom ). One can imagine the bed randomly varying these three parameters, and receiving Feedback with each variation that begins to direct if a parameter needs to be turned 'up' (say, higher temperature) or down (say, lower music volume). As the sleeper begins to lay increasingly still in bed, the bed will arrive at a state at wherein (all other things being equal), any change to any parameter will result in the motion of the sleeper increasing rather than decreasing. The bed has now arrived at a fitness peak.  

What is interesting about this example is that we can imagine a landscape where an agent is on a local peak, while remaining unaware of higher peaks within the landscape or Phase Space.  So perhaps someone sleeps well EITHER with low music and a warm, firm bed, OR with loud music and a cool, soft,  bed. The peak with louder music and a cooler bed might result in the sounder sleep of the two, but if the bed randomly adjusts and initially arrives at the first regime of behavior, it may not be able to arrive at the second without passing through states that decrease fitness prior to improving fitness. It will, therefore, be difficult to move to the higher peak without 'jumping' from one part of the fitness terrain to another. 

These jumps can be assisted by what is called a system Perturbation, which disrupts the landscape in such a way as to jolt an agent away from the peak it has been inhabiting and thereby explore a different part of the potential landscape. Without these perturbations, agents are limited to exploring smooth variations in behavior that move into the what Stuart Kauffman describes as the 'adjacent possible'.

The idea of a fitness peaks is part of the broader concept of Fitness within a Complex System. 

related terms:

Fitness Landscape

Attractor States


Cite this page:

Wohl, S. (2017, 16 June). Fitness Peaks. Retrieved from https://kapalicarsi.wittmeyer.io/definition/fitness-peaks

Fitness Peaks was updated June 16th, 2017.

In Depth... Fitness Peaks

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  • This is a list of Terms that Fitness Peaks is related to.

    Complex Adaptive Systems become more 'fit' over time. Depending on the system, Fitness can take many forms,  but all involve states that achieve more while expending less energy.

    What do we mean when we speak of Fitness? For ants, fitness might be discovering a source of food that is abundant and easy to reach. For a city, fitness might be moving the maximum number of people in the minimum amount of time. But fitness criteria can also vary - what might be fit for one agent isn't necessarily fit for another. For example, what makes a hotel room 'fit'? Is it location, or price, or cleanliness, or amenities, or all of the above?  For different people, these various factors or parameters have different 'weights'. For a backpacker traveling through Europe, maybe the price is the only thing worth worrying about, whereas for a wealthy business person it may not factor in at all.

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    Complex Systems can unfold in multiple trajectories. However, there may be trajectories that become more stable of 'fit'. Such states are considered 'attractor states' to which a system tends to gravitate.

    Attractor States or 'basins of attraction' and can be visualized as part of a fitness landscape.

    Complex Adaptive Systems do not obey predictable, linear trajectories. They are Sensitive to Initial Conditions and small changes in these conditions can lead the system to unfold in entirely unexpected ways. That said, some of these 'potential unfoldings' are more likely to occur than others. We can think of these as 'attractor states' to which a system - out of all possible states - will tend to gravitate.  However, these attractor states may also shift over time, and are subject to system disruptions or  what is referred to as a Perturbation. Attractor states can also emerge gradually over time, as the system evolves, but once present can reinforce itself by constraining the actions of the agents forming the system. Thus we can think of Silicon Valley as being an emergent attractor for tech firms, that has, over time, reinforced its position. When a system finds itself 'trapped' in a basin of attraction (such that it cannot explore other potential configurations that may be more fit, it is considered to be in an Enslaved States .

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