cas/definition/feature.php (core concept)
Diagram: Fitness

Fitness

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 <i>parameters</i> 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.

Accordingly, the idea of fitness in any complex system is not necessarily a fixed point. There can be many different kinds of fitness, and we need to examine the system to determine what factors are at play.

That said, there are certain principles that remain somewhat more constant, and this pertains to the idea of minimizing processes. We can imagine that certain behaviors in a system require more or less energy to perform. If an ant wants to find food, it prefers to find a source that takes less time to get to than one that is further away. Further, a bigger source of food is better than a smaller source of food, as more ants in the colony can benefit. Complex systems generally gravitate towards regimes that therefore in some way minimize energy expenditure to achieve a particular goal. However, this depends on the nature of the goal.

Example: Returning to the example finding a hotel room, consider the popular website 'Airbnb' as a complex adaptive system. Here, two sets of bottom-up agents (room providers and room seekers) coordinate their actions in order to have useful room occupancy patterns to emerge. Some of these patterns might be unexpected. For example, a particular district in Paris might emerge as a very popular neighborhood for travelers to stay in, even though it is not in the center of the city. Perhaps it is just at a 'sweet-spot' in terms of price, amenities, and access to transport to the center. This is an example of an emergent phenomena that might not be predictable but nonetheless emerges over the course of time. In that case, rooms in that district might be more 'fit' than in another, because of these interacting parameters that are highly appealing to a broad swath of room-seekers.

So in what way is the above example 'energy minimizing'? We can think of the room seekers as having different packages of energy they are willing to expend over the course of their travel. One package might hold their money, one might hold their time, and one might hold their patience to deal with irritations (noisy neighbors that keep them from sleeping, or willingness to tolerate a dirty bathroom...). Each agent in the system is trying to manage these packets of energy in the most effective way possible to preserve them for other needs. So if a room is close to the center of the city, it might preserve time energy, but this needs to be balanced with preserving money energy.

We can begin to see that fitness is not going to come in a 'one size fits all' form. Some agents will have more energy available to spend on time, and others will have more energy to go towards money. Further, an agent in the system might be willing to spend much more money if it results in much more time being saved, or vice versa. We can imagine that an agent might reach a decision point where these two equally viable trajectories are placed in front of them. The choice of time or money might be likened to a flipping of a coin, but the resulting 'fit' regime might appear as very different.

In order to better understand these dynamics, two features of CAS, that of a Fitness Landscape and ideas surrounding Bifurcations, clarify how CAS can unfold in multiple fit trajectories, but despite these differences the underlying principles of energy minimizing holds true.

In the above example the agents (room seekers), employ cognitive decision-making processes to determine what a 'fit' regime is. But physical systems also gravitate to these energy minimizing regimes.

Example: When molecules in a soap bubble solution are subject to being blown through a soap wand, nobody tells them to form a bubble, and the molecules themselves don't consider this outcome. Instead, the bubble is the soap mixture's solution to the problem of finding a surface area that minimizes surface area and therefore frictions. The soap bubble can  therefore be considered as an energy minimizing emergent phenomena  (if you want a detailed explanantion, then follow the link to an article on the subject: note the phrase, 'a bubble's surface will minimize until the force of the air pressures within is equal to the 'pull' of the soap film'). We can also think of a sphere as being the natural Attractor States of a soap solution seeking to absorb maximum air with minimum surface - or doing the most with the least.

 


Cite this page:

Wohl, S. (2021, 7 July). Fitness. Retrieved from https://kapalicarsi.wittmeyer.io/definition/fitness

Fitness was updated July 7th, 2021.

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This is a list of People that Fitness is related to.

Evolution

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

    A regime of particular fitness that an agent can occupy.

    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}} Learn more →
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  • This is a list of Urban Fields that Fitness is related to.

    Increasingly, data is guiding how cities are built and managed.  'Datascapes' are derived from our actions but also then steer them. How do humans and data interact in complex ways?

    More and more, the proliferation of data is changing the ways in which we inhabit space... and so forth.
    Learn more →

    How can our cities adapt and evolve in the face of change? Can complexity theory help us provide our cities with more adaptive capacity to respond to uncertain circumstances?

    Increasingly, we are becoming concerned with how we can make cities that are able to respond to change and stress. Resilient urbanism takes guidance from some complexity principles with regards to how the urban fabric can adapt to change.
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    Cities traditionally evolved over time,  shifting to meet user needs. How might complexity theory help us  emulate such processes to generate 'fit' cities?

    Some Urban thinkers consider how the nature of the morphologic characteristics of the city help enable it to evolve, incrementally, over time. This branch of Urban Thinking considers time and evolution as key to generating fit urban spaces
    Learn more →

    Across the globe we find spatial clusters of similar economic activity. How does complexity help us understand the path-dependent emergence of economic clusters?

    Evolutionary Economic Geography (EEG) tries to understand how economic agglomerations or clusters emerge from the bottom-up. This branch of economics draws significantly from principles of complexity and emergence, seeing the rise of particular regions as being path-dependent, and trying to understand the forces at work that drive change for economic agents - the firms that make up our economic environment.
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  • This is a list of Key Concepts that Fitness is related to.

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    Navigating Complexity © 2015-2021 Sharon Wohl, all rights reserved. Developed by Sean Wittmeyer
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