Adaptation

What adaptive traits do the individuals have? What rules do they have for making decisions or changing behavior in response to changes in themselves or their environment?

This concept identifies the agents’ adaptive behaviors: what decisions agents make, in response to what stimuli. All such behaviors should be identified and described separately. The description should include components of behavior such as: the alternatives that agents choose among; the internal and environmental variables that affect the decision; and whether the decision is modeled as “direct objective seeking”, in which agents rank alternatives using a measure of how well each would meet some specific objective (addressed in the Objectives concept, below), or as “indirect objective seeking”, in which agents simply follow rules that reproduce observed behaviors (e.g., “go uphill 70% of the time”) that are implicitly assumed to convey success.
Many ABMs include only very simple behaviors that can be hard to think of as adaptive or even as decisions. An example is the Schelling segregation model: in it, agents simply move to a new randomly-selected location when too few of their neighbors are the same color. However, even such simple responses should be described as adaptive behaviors in ODD: the agents decide whether to stay or move by testing whether an objective measure—the percentage of neighbors having their color—is below a threshold value. At the other extreme are ABMs that use complex evolved behaviors: each agent has an internal decision model such as an artificial neural network that is evolved in the ABM to produce useful adaptive behavior. This approach can still be described in the framework provided here: what alternatives are considered by the decision model, what its inputs are, and how its outputs are used to determine behavior. An addition step for such models is to also describe the “training conditions”: what problem were the agents given to solve in the evolution of their decision models? Such adaptive behavior models can be considered indirect objective seeking because the agents have been trained via evolution to produce behaviors successful under the training conditions. Describe, for each adaptive behavior of the agents: • What decision is made: what about themselves the agents are changing. • The alternatives that agents choose from. • The inputs that drive each decision: the internal and environmental variables that affect it. • Whether the behavior is modeled via direct objective-seeking—evaluating some measure of its objectives for each alternative—or instead via indirect objective-seeking—causing agents to behave in a way assumed to convey success, often because it reproduces observed behaviors. • If direct objective-seeking is used, how the objective measure is used to select which alternative to execute (e.g., whether the agent chooses the alternative with the highest objective measure value, or the first one that meets a threshold value).

• The model households have one adaptive behavior: deciding whether or not to move to another location and, if so, selecting a new location. The decision of whether to move is modeled as direct objective seeking: a household moves if its objective measure (Objectives, below) is below a “tolerance threshold”. This approach can be considered a type of satisficing—making a decision to achieve an acceptable level of the objective instead of maximizing it. The tolerance threshold is a model parameter named %-similar-wanted. When a household does decide to move, it selects a new location from those not currently occupied using a stochastic process described below (the “move” submodel).
• The adaptive behavior of investor agents is repositioning: the decision of which neighboring business to move to (or whether to stay put), considering the profit and risk of these alternatives. Each time step, investors can reposition to any unoccupied one of their adjacent patches or retain their current position. Which patch to select is modeled as direct objective seeking using optimization: investors select the patch that provides the highest value of the objective measure explained below.
• The primary adaptive behavior of dog packs is dispersal: the pack decides whether its subordinate dogs leave the pack in hopes of establishing a new pack. This behavior is modeled using indirect fitness seeking: stochastic rules cause the pack to select each alternative with a frequency similar to the frequency observed in real wild dogs, with the implicit assumption that these observed frequencies occur because they make dogs relatively successful at becoming alpha adults and, therefore, being able to reproduce. Two simple rules are used to model the dispersal behavior. If a pack has only one subordinate dog of its sex, it decides randomly whether this dog “disperses” by forming a new disperser group entity; the probability of dispersing is 0.5. If the pack has more than one subordinate dog of the same sex, those dogs always form a disperser group and leave the pack.”