Preface"We have to look for routes of power our teachers never imagined, or were encouraged to avoid.
- Thomas Pynchon, Gravity's Rainbow"
p.3"The process of scientific discovery is, in effect, a continual flight from wonder. - Albert
Einstein, Autobiographical Notes"
p.4"The field of complex systems must direct its 'flight from wonder' toward discoveries that 'make
the wonderful and complex understandable and simple.' "
p.8"Kenneth Boulding summarized science as consisting of 'testable and partially tested fantasies
about the real world.' The science of complex systems is not a new way of doing science but rather one in which new fantasies
can be indulged."
p.11"Regardless of the approach, the quest of any model is to ease thinking while still retaining
some ability to illuminate reality."
p.35"We begin with a discussion of the basics of scientific modeling... despite its importance,
most people do not want to discuss it, and no matter how much you read about it, it just doesn't seem the same when you actually
get around to doing it... Effective models require a real world that has enough structure so that some of the details can
be ignored."
p.39"The success of a particular model is tied to its ability to capture the behavior of the real
world."
p.36,37"One of the best models that we encounter in our daily experience is the road map... Maps
are valuable for a variety of reasons. One reason is that they leave out a lot of unnecessary details. In doing so,
they... allow us to focus on the questions that we most care about... Good maps not only allow us to predict key features
of the world, but they also enable us to discover new phenomena."
p.43"Creating a model is much like trying to solve a brain teaser. Finding such solutions is often
an extremely difficult task... Yet, once discovered, the answer has strong intuitive appeal and appears all too obvious."
p.44"Much of the focus of complex systems is on how systems of interacting agents can lead to emergent
phenomena... The usual notion put forth underlying emergence is that individual, localized
behavior aggregates into global behavior that is, in some sense, disconnected from its origins."
p.50"When interactions are not independent, feedback can enter the system. Feedback fundamentally
alters the dynamics of a system. In a system with negative feedback, changes get quickly absorbed and the system gains stability.
With positive feedback, changes get amplified leading to instability."
p.59"Currently, the predominant tool in economic theorizing is the development of mathematical models
derived from a set of first principles... we can use these models to make predictions... These tools provide both a ready
set of simplifications for understanding the world and a process by which the implications of these simplifications can be
derived in a consistent way."
p.62"All tools are designed to simplify some task. If the task we face corresponds with this simplification,
then the tool will be of value."
p.62"By attacking problems on numerous fronts, breaches in nature's walls inevitably appear. Though
it may be difficult to predict on which front the walls will first crumble, openings on one front are likely to lead to progress
on another."
p.65"the goal of theory is to make the world understandable by finding the right set of simplifications.
Modeling proceeds by deciding what simplifications to impose on the underlying entities and then, based on those abstractions,
uncovering their implications. The types of computational models we wish to focus on here are those in which the abstractions
maintain a close association with the real-world agents of interest, and where uncovering the implications of these abstractions
requires a sequential set of computations involving these abstractions.
The property of close association is often known as 'agent-based' modeling... A marginally
better term might be modeling using 'agent-based objects' versus 'abstraction-based objects' "
p.69"To create models that go beyond our initial understanding, we need to incorporate frameworks
for emergence. That is, we need to have the underlying elements of the model flexible enough so that new, unanticipated features
naturally arise within the model."
p.72"The ability of a model to generalize is linked closely to its inherent frameworks for emergence."
p.72"Computational models are often thought to be brittle, in the sense that slight changes in one
area can dramatically alter their results... For good modeling we need to keep in mind the brittleness of our tools and actively
work to avoid producing theories that are too closely tied to any particular assumption."
p.83"Many of our existing analytic tools avoid an emphasis on dynamic processes and focus on equilibrium
states... In natural systems, however, equilibria are usually associated with the death of the system. The conditions that
favor equilibrium analysis are likely the exception rather than the rule in many complex adaptive social systems. If so, the
techniques that we traditionally use to analyze these systems may be like trying to 'understand running water by catching
it in a bucket.' "
p.84"Social scientists have often recognized the importance of dynamic analysis but have been very
constrained by their tools. According to Von Neuman and Morgenstern (1944, 44) [JLJ - Theory of Games and Economic
Behavior ], in their seminal work on game theory, 'We repeat most emphatically that our theory is thoroughly
static. A dynamic theory would unquestionably be more complete and therefore preferable. But there is ample evidence from
other branches of science that it is futile to try and build one as long as the static side is not thoroughly understood.'
"
p.84"In situations where equilibria are nonexistent or transient paths are long, understanding the
dynamics is critical."
p.94,95"Given the limits of information processing, agents must actively ignore most of the potential
information they encounter... We suspect that a full analysis of how agents selectively attend to information will provide
some interesting scientific opportunities... agents may develop ways, such as statistics, to summarize the flow of incoming
information so that it is easily stored and used... agents may... influence the actions of others... the reality is that most
agents exist within a topology of connections to other agents, and such connections may have an important influence on behavior."
p.96"In social systems, we may want the agent behavior to aggregate in such a way that the system
achieves some goal."
p.101"Right Concentration [JLJ - a model construction principle ] is the focus
of the model - namely, it requires the model to be just sufficient to capture the phenomenon of interest. Models always have
contexts, and what works well in one context may fail in another... Models of complex systems phenomena should be simple,
not complicated. This point often seems to get confused and twisted in various ways, but the point of modeling - whatever
the target - is to simplify an otherwise overly complex world. Thus, even when the resulting behavior is complex, the underlying
model should be simple."
p.114"Modeling any system is often an exploratory process that requires both induction and deduction.
You begin by making a simple set of assumptions and see where they lead. From this experience you attempt to create better
models or deduce more exact results."
p.139-140"the structures necessary for delicate behavior require an underlying system that is rich
in possibilities. In essence, we need a quivering system that will fall into the right state with only a gentle tap. In such
a system, an improper tap can lead to very unpredictable results.
Adaptive systems have to deal with the tension between the benefits of achieving precise
behavior and the cost of increased system fragility. One hypothesis is that adaptive systems will have a bias toward emphasizing
simple structures that resist chaos over more complicated ones that handle difficult situations."
p.141"Far better an approximate answer to the right question, which is often vague, than the exact
answer to the wrong question, which can always be made precise - John Tukey, Annals of Mathematical Statistics"
p.177"adaptation can alter the critical behavior of the system... With adaptive agents, the system
configures itself in a way that mitigates the overall risk by preventing criticality from emerging. In essence, the adaptive
actions of the individual agents lead the system away from the critical regime and more toward what an omniscient designer
attempting to balance risk and stability would create."
p.214-215"A key contribution of complex systems has been a better appreciation of the power and
mechanism of emergence... Perhaps many features of social systems are the result of self-organization... Models of emergence
also provide insights into the robustness of the underlying system, as the essence of emergence requires entities to be able
to maintain their core functionality despite what are often radical changes from both within and without. Using emergence
ideas, we can begin to understand the robustness of systems such as markets, cultures, and organizations like firms and political
parties."
p.220"Chess has fascinated players in its modern version for over half a millennium... chess...
has a well-defined game tree that, in theory, we could work our way through and develop an optimal strategy. If our cognitive
abilities were just a bit higher, all the fuss about chess might be a bit embarrassing... Like humans, computers are unable
to generate the entire game tree for chess except toward the end of the game. Therefore, programs must rely on various heuristics...
and other various means to decide on their moves."
p.236"Inherent in many complex adaptive social systems is a degree of robustness... which details
of the agents matter in terms of maintaining the system's coherence? Second, we can consider the robustness of the entity
to perturbations in the environment. What does it take for a system to persist in the face of external changes? Alternatively,
we could frame the question as uncovering the factors that make a given system brittle [JLJ - the opposite of robust
]."
p.246"Making sure that your model has just enough of the right elements and no more is the most
fundamental practice for any kind of modeling, and computational work is no exception... Scientific modelers must aim for...
simplicity and clarity. Modeling is like stone carving: the art is in removing what you do not need.
It is often easy to recognize a simple, well-formulated model after the fact, as such models
have strong intuitive appeal... Good models strip phenomena down to their essentials, yet retain sufficient complication to
produce the needed insights."
p.249"Often it is possible to create computational models with simple, flexible frameworks that
'get filled in' by the computation... it is the evolution of the system that fills in the details. A well-designed framework
puts very few a priori constraints on the model, and thus the outcome is rich in possibilities. Such frameworks provide enough
flexibility so that the model can explore areas that were not fully anticipated by the researcher... Note that even the simplest
of frameworks can result in outcomes that are difficult to understand."
p.254"Scientific judgments in this area [computational modeling] should focus not on the computer
per se, but on the quality and simplicity of the model, the cleverness of the experimental design, and the new insights
gained by the effort."
