a fitness landscape also called an adaptive landscape is a model that comes from biology where it is used to describe the fitness of a creature or more specifically genotypes within a particular environment the better suited the creature to that environment the higher its elevation on this Fitness landscape will be as such it visually represents the dynamics of evolution as a search over a set of possible solutions to a given environmental condition in order to find the optimal strategy which will have the highest elevation on this landscape and receive the highest payoff (View Highlight)
this model has been abstracted and applied to many different areas in particular within computer science business management and economics but is equally applicable to all complex adaptive systems (View Highlight)
within this more generic model of a location on the landscape is a solution to a given problem the elevation captures how functional that solution is and solutions that are similar in nature are typically placed close to each other (View Highlight)
there are two main things we need to consider firstly the type of landscape we are dealing with and secondly the types of strategies we might use given these different landscapes (View Highlight)
gents within complex adaptive systems can typically only respond to local level information whether we are talking about a trader in a financial market or a herd of deer looking for pasture these agents do not have complete information of their environment they can only access and thus respond to a limited amount of typically local level information and they need to have a strategy for processing this information and generating an optimal response this strategy is essentially just an algorithm we will call this the Explorer or exploit algorithm because an agent has fundamentally just two options to either exploit their current position within the landscape or invest resources to go exploring for new solutions that is to say looking for higher Peaks (View Highlight)
xt we will turn up the distribution of solutions here the topology will develop many local Peaks of varying height this landscape corresponds to a problem that involves a set of interacting variables there are many different variables and different combinations between them giving us lots of different possible solutions designing a car would be an example of this we might want it to be fast and low-cost but if we put a bigger engine in it to make it go faster this would require a stronger chassis which would add to the costs and there would be of course many more interacting factors involved allowing for many different possible solutions but some would still be better than others applying our greed algorithm here would result in an agent getting stuck on the first local peak it comes - which is unlikely to be the global optimal solution what is needed is a much greater initial investment in exploring allowing the agent to go up and down many times while also over a prolonged period gradually reducing the amount of time the agent is allowed to go downhill thus gradually closing in on a global optimal solution without getting stuck on local Peaks (View Highlight)
in these dynamic environments this is no longer the case as the goal is changing and the agents need to stay changing with it if we then turn up the interdependency between the actions that agents take the environment will become more dynamic as the topology is being continuously shaped and reshaped by the actions and reactions of the agents to each other with agents needing to be continuously adapting (View Highlight)