Effects of Limited Resources in 3D Real-time Simulation of an Extended
ECHO Complex Adaptive System Model

Dana M. Dominiak^∗^, Frank Rinaldo^^, Martha W. Evans^¶^

^∗^Department of Computer Science, Illinois Institute of Technology,
Daniel F. and Ada L. Rice Campus,\
206 East Loop Drive, Wheaton, IL 60187-8489 USA

^^Comnet International Co, 3030 Warrenville Road, Suite 320, Lisle, IL
60532 USA

^¶^Department of Computer Science, Illinois Institute of Technology,
Stuart Building, Chicago, IL 60616 USA

**Abstract.** An evolutionary model of adaptive agents called 'ECHO' was
proposed by John Holland. ECHO is a first step toward mathematical
theory in the field of complex adaptive systems. Researchers in numerous
disciplines have used the existing ECHO simulation both to model and to
explain complex system behaviors. This paper describes the effects of
limited resources in a 3D simulation of an extended Holland ECHO model.
In this simulation, adaptive agents move about the ECHO terrain and
interact with other agents in real-time. Adaptive agents are bred using
a genetic algorithm. The model's environment contains limited resources,
represented as symbols. Elaborate relationships are developed by the
agents to utilize resources through both competition and cooperation.
Researchers have a better tool by which to identify and explain complex
adaptive system behavior by observing the emergence of complexity first
hand.

# Introduction {#introduction .INTRODUCTION}

The genetic algorithm (GA) has been more successful finding solutions to
complex, nonlinear problems from the \"bottom-up\" than traditional
\"top-down\" methods. However, a challenge in the search of a complex
solution space has always been forcing a GA to find something other than
local optima. This is caused by evolution's tendency to quickly fill a
niche and therefore effectively abandon the search for alternative
solutions.

A complex adaptive system (CAS) describes a model containing intricacy
and fluidity found in natural systems. As relatively simple agents
interact in a dynamical setting, global behaviors will emerge. These
global effects emerge from more simple, local interactions. CAS have
typically been used to model biological and other natural systems, which
are greatly different than the specific minimum/maximum search problems
that the GA has traditionally been used to solve.

Complex and living systems are difficult to understand by top-down
analysis. Instead, they are better understood by synthesis \[1\].
Therefore, such models should be implemented as computer simulations
\[2\]. Understanding of biological processes in particular is one of the
primary purposes of studying CAS, however, the organization of nature in
the broader sense is an even greater justification for such models. CAS
theory seeks to identify rules and processes which describe all complex
systems, such as economies, ecosystems, animal and insect population
behaviors, immune systems, embryogenesis, etc. Control or prediction of
complex systems might become possible if "lever points" and mechanisms
can be found which allow for a better understanding of these systems
\[3\].

It is hoped that by discovering the processes involved in creating
complexity from adaptive simulation, the rules governing real living
systems will be discovered \[4, 3, 5, 6\].

Creating a system that resembles natural systems is a main purpose for
implementing such a simulation. It is likely that the causes of
emergence will be identified with such a 'constructionist' \[6\] tool,
eventually leading to theory, and ultimately, to control and prediction
of natural systems.

Additionally, it is often more cost-effective to develop many simple
agents than it is to develop a single complicated agent \[1, 7\].
Therefore, any improved CAS techniques are valuable to industry and
scientists in the hopes of evolving better methods to solve difficult
machine problems and facilitate natural system simulations.

# THE ECHO MODEL

The ECHO model was initially proposed by John Holland \[3\], father of
genetic algorithms, as a vehicle for carrying out "thought experiments"
on CAS. Numerous ECHO computer simulations have been subsequently
implemented. These simulations capture the fluidity and complexity of
Holland's model better than pure cognitive exercises. A screen image of
the extended ECHO model discussed in this paper can be seen in Figure 1.

Most ecological models omit evolutionary methods \[8\]. However, ECHO
consists of a population of agents that are modified over time by a GA.
The agents adapt and evolve by selective pressures imposed by their
environment, including other agents.

![](media/image1.png){width="3.079861111111111in"
height="2.3090277777777777in"} The agents\' world in ECHO consists of
limited resources in the form of symbols: {a, b, c, d, e, ... }.
Competitive and cooperative relationships will be developed among agents
in order to utilize these limited resources \[8\].

Non-linearities inherent in complex systems are difficult to model
analytically \[8\]. Therefore, simulations such as ECHO facilitate the
analysis of these phenomena. Such a model will help to identify and
clarify the properties of CAS, hopefully leading to mathematical theory.

ECHO agents must compete for limited resources. Each site in the world
might contain agents, an inflow of resources, or both.

Interactions are dictated by offense and defense tags, which are
possessed by each agent. The model possesses an implicit fitness
measure, an important characteristic for inaugurating complexity \[3,
9\].

ECHO was used by environmental researchers to model food web complexity
\[10\]. In a paper describing this model, researchers were able to show
that individual behaviors (i.e. local rules) influenced the global food
web\'s complexity. It was proved that explicitly deriving differential
equations was not necessarily the most facile or accurate method for
modeling food web complexity.

## EXTENSIONS TO ECHO

Mechanisms and procedures were added to Holland's basic ECHO model to
emphasize spatially oriented characteristics and physical hierarchies
inherent in natural systems. A primary goal of the initial ECHO design
was stripping away unnecessary details while maintaining diverse
complexity. A principal goal of our extended model was retaining this
design criterion.

While Holland's ECHO models are an extraordinary step toward CAS theory,
they are far from replicating the complexity present in actual natural
systems. This lucidity was no accident, and helps to identify lever
points and mechanisms of CAS by utilizing the simplest of approaches.
However, a critical role in the evolution of adaptive agents has
recently been shown to be the environment \[4, 9\]. Therefore, perhaps
the minimal addition of some environmental factors to ECHO are well
justified.

The extensions to ECHO include agent resource recycling, resource
adhesion tags, resource hierarchies, and resource decay.

**Figure 1.** A Screen of the Extended ECHO Visualization

![](media/image2.png){width="6.5in"
height="1.6868055555555554in"}**Figure 2.** An Extended ECHO Agent
Showing Tagging and New Exhaust of Resources.

The extended ECHO agent is pictured in Figure 2. The agent contains the
classic ECHO offense and defense tags, as well as a new exhaust tag,
allowing resources to be recycled back in the environment by a method
other than direct agent interaction. The evolutionary model implemented
here using a classic GA and tagged agents has led to the emergence of
both competitive and cooperative relationships such as parasitism and
symbiosis.

TABLE 1. Evolutionary Techniques

  ----------------------- -----------------------------------------------
  **General Technique**   **Implementation**

  Randomness              Mutation, Simulated Annealing

  Co-evolution            Parasitism, Symbiosis, Competition,
                          Cooperation, Arms Races

  Speciation              Tagging, Multiple Populations
  ----------------------- -----------------------------------------------

# Conclusion

This CAS visualization tool allows the observation, interpretation and
explanation of emergent and complex phenomena found in natural systems.
Such a tool allows researchers to observe complex interactions among
simple agents, re-run interesting evolutionary models from a known
initial condition, and observe the relationship of resources among
adaptive agents. Such a tool is a good first step in the identification,
prediction, and control of emergent complex phenomena.

# Acknowledgments

Thanks to Cristian Soulos and Pascal Pochol for their work on the 3D
engine and low-level system routines. Research funding and hardware
resources provided by Webfoot Technologies, Inc.

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