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10 B. Edmonds and R. Meyer

All of the approaches described in these three chapters are aided by good,
clear documentation. Chapter 15 describes a way of structuring and performing
such documentation that helps to ensure that all necessary information is included
without being an overly heavy burden.

1.3.3 Mechanisms Part

The third part considers types of social mechanisms that have been used and
explored within simulations. It does not attempt to cover all such approaches, but
concentrates upon those with a richer history of use, where knowing about what has
been done might be important and possibly useful.
Chapter 16 takes a critical look at mechanisms that may be associated with
1
economics. Although this handbook is not about economic simulation, mechanisms
from economics are often used within simulations with a broader intent. Unfortu-
nately, this is often done without thinking so that, for example, an agent might be
programmed using a version of economic rationality (i.e. considering options for
actions and rating them as to their predicted utility) just because that is what the
modellers know or assume. However, since economic phenomena are a subset of
social phenomena, this chapter does cover these.
Chapter 17 surveys a very different set of mechanisms, those of laws, conventions
and norms. This is where behaviour is constrained from outside the individual in
some way (although due to some decision to accept the constraint from the inside
to differing degrees). Chapter 18 focuses on trust and reputation mechanisms, how
people might come to judge that a particular person is someone they want to deal
with.
Chapter 19 looks at a broad class of structures within simulations, those that
represent physical space or distribution in some way. This is not a cognitive or social
mechanism in the same sense of the other chapters in this part, but has implications
for the kinds of interactions that can occur and indeed facilitates some kinds of
interaction due to partial isolation of local groups.
The last two chapters in this part examine ways in which groups and individuals
might adapt. Learning and evolution are concepts that are not cleanly separable;
evolution is a kind of learning by the collection of entities that are evolving and
has been used to implement learning within an individual (e.g. regarding the set
of competing strategies an individual has) as well as within a society. However,
Chap. 20 investigates these concepts primarily from the point of view of algorithms
for an individual to learn, while Chap. 21 looks at approaches that explicitly take
a population and apply some selective pressures upon it, along with adding some
sources of variation.

1 There is an extensive handbook on this (Tesfatsion and Judd 2006).