The Development of Collective Structure and its Response to Environmental Change


Norman L. Johnson

Theoretical Division

Los Alamos National Laboratory

MB B216, Los Alamos, New Mexico USA


How do collective processes in decentralized, self-organizing systems respond to environmental change? What are the contrasting roles of collective structures and innovative components (variation, diversity, entropy), and how do these roles change with different rates of environmental change? To answer these questions, a simple self-organizing system is examined – a simulation of foraging for food by ants in the presence of environmental change. This model system has been argued to be similar in dynamics to decentralized components of many collective systems - ecologies, economies, knowledge systems, societies, political systems, etc. The simulations are first shown to illustrate a developmental view of evolving systems, captured by the developmental stages of Formative, Co-Operational and Condensed. The effects of different rates of environmental change are then presented. For small rates of change, the system productivity is largely unchanged. As the rate increases, innovative information becomes more important to sustaining the productivity. As the rate further increases, episodic failure is observed as stabilizing collective structures fail, and the system regresses to earlier developmental stages. The collective structures are shown to inhibit the performance of the system as a whole in rapidly changing environments. A quantitative measure is developed that captures the efficacy of the collective structure. A variation of the system with a mechanism for sustaining collective structures is found to be more sensitive to environmental change, duplicating the decline in productivity observed in aging systems.


The following study begins with the assumption that decentralized, self-organizing processes are key components to many systems and then focuses on what insights can be gained about the nature of these processes in a specific model system in the presence of different rates of environmental change. “Environmental change” is used to describe aspects of a system (e.g., boundary conditions or coupled systems) that are typically assumed constant or slowly changing. The understanding that environmental stability is required for the development of ecological systems has been present since early studies (Conrad 1983), but a general understanding of the dynamics across the different stages, with and without environmental change, appears to be missing. The relevance of such an investigation is becoming more apparent in a world has fewer components that are in

The object of this study is an agent simulation of an ant foraging system, which has been extensively studied both in nature and computationally (Bonabeau et al 1999). This system represents the popular example of an “organism” (system of processes) that has no centralized coordination of its components (agents), where the capability of the components are simple compared to the capabilities of the “organism” as a whole, and where the fundamental processes (reinforcement of individual pheromone trails) have been used to solve a variety of challenging problems (Bonabeau et al 1999).


The agent simulation tool chosen is StarLogo, a cross-platform, public-domain software that is maintained by M.I.T’s MediaLab and available at their web site[1]. This software was selected over others because of its simplicity to use and modify, because of its availability to enable experimentation beyond the studies here, and because it provides a documented example of ant foraging[2].

The example ant-foraging problem in StarLogo is defined as follows (see Figure 1). One hundred ants using identical rules forage on a periodic square domain (e.g., if an ant leaves the top, it reenters the bottom). The domain is discretized into 101 divisions in the two dimensions. The agents have only three rules of action: 1) if food is found, pick it up and return to the nest, leaving an evaporating pheromone trail behind, 2) if an agent has food and is at the nest, then drop the food and reverse direction 180 degrees and search for food, 3) when searching for food, follow the shared information (pheromones) to the source, otherwise search randomly. In the two operations of returning to the nest by the radial pheromone field (see Figure 1) and by following the shared information to the source, an estimate of the gradient of the “information” field is made and the agent attempts to follow the increasing gradient. Note that the radial field for returning to the nest approximates a globally aware mechanism for returning to the nest (Wohlgemuth, et al 2001). The agents move one space per time step (or time unit), so in 50 time units an agent can move from the nest to the closest periodic boundary in a direct line.

While the above model is unsophisticated by comparison to scientific models (Bonabeau et al 1999), it does capture the essential features of foraging behavior: of exploiting the closest sources first, of requiring additional resources to optimally exploit food sources far from the nest (longer trails require more reinforcement), and of exploiting one food source at a time when two sources are equidistant. By expressing a variety of realistic behavior, the example simulation is a reasonable choice for the present study. While not examined, likely other implementations would duplicate the same conclusions as the present study.

The example allows the user to modify the evaporation rate and the diffusion rate of the pheromones in order to examine the effect of these parameters on the performance of the system. The diffusion mimics the spatial diffusion by Fick’s Law: at each time step a percentage of the pheromone in a storage location will be transferred to its eight neighboring locations. For example, if the diffusion rate is set to 12%, then 0.12 of the pheromone will be transferred (0.015 of the pheromone to each of the eight neighboring locations). The evaporation is the reduction at each time step of the pheromones in the entire domain by a percentage set by the evaporation rate. For example, if the evaporation rate is set to 4%, the pheromone level at each storage location will be reduced by 0.04 of its value at each time step. Therefore, an evaporation rate of 100% removes all pheromones at each time increment.

Figure 1: Identification of the components of the simulation. Not shown is the permanent pheromone field that is created at initialization that enables the agent to return to the nest; it decreases radially from the nest to the boundaries.

The original example is modified in one way (aside from a variety of diagnostic additions described shortly) and the setup file is available at the author’s website[3]. The original movement of the agent in the domain was more random and was set to vary at each step by an increment of plus or minus 40 degrees from its current direction (sampled from a uniform distribution). In the following simulations this variation is reduced to plus or minus 10 degrees for random foraging and plus or minus 5 degrees for returning to the nest. These changes tend to cause less randomness in the individual movement from a straight line, but no qualitative differences are observed in the results reported below, and quantitative differences are within the variations from run-to-run of the simulations examined. The values for all following simulations (except where noted) use the evaporation and diffusion rates of 4% and 12%, respectively.

Finally, the setup (initial) conditions of the original example are modified. Instead of three stationary food sources of a limited supply (given sufficient time, each source will be used up), one stationary (or moving) source of unlimited supply is created in the domain. When stationary, the unlimited nature of the supply enables the exploitation of the source to reach steady state (to within the natural variation of the system).

To enable the foraging simulations to have broader applicability, the following nomenclature is used and subsequent diagnostics are based on these definitions (see Figure 1). The agents (models of ants) leave information (pheromones) on the domain when they are returning to the nest with food. If the pheromone cloud persists on the domain as reinforced by the combined pheromones of the agents, it is called a collective structure. Note that for the distance of the source and the evaporation and diffusion rates chosen, a single agent cannot sustain a collective structure. A collective is a group of agents that have gotten food and are still within the collective structure. Agents in the collective are either carrying food to the nest or do not have food but by design, they are following the maximum gradient of shared information (pheromones) that might lead to a source. Agents that are not part of the collective are individuals (also called innovators, when the individuals play an essential role). Note that an individual can either be outside the collective structure and randomly searching or can be within the collective structure but not have found the source yet. The above concepts of a collective and individuals are only for the purposes of analysis and do not affect the simulation in any way.

 The productivity of the system as a whole is defined as the rate that the source is exploited. Similarly, the productivity of the collective and individuals are the rates that the collective and individuals exploit the source, respectively. The productivity is defined as the rate of food taken from the source. (An alternative definition of productivity is based on the deposition of food at the nest, but it is not used in order to avoid the intrinsic time delay in this measure, caused by the transit time of the agents returning to the nest.)


To aid in the analysis of the effect of environmental change, the following view of the developmental stages of self-organizing systems is introduced for stable environments and is discussed in detail elsewhere (Johnson 2000; Johnson 2002). This perspective is closely related to the developmental theory proposed by Salthe for biological (ecosystems, etc.)(Salthe 1989; Salthe 1993a; Salthe 1999) and sociological systems (society, economies, organizations) (Salthe 1993b). To date, the primary difference between the two developmental views is that Salthe’s focuses on the interplay of developing structure and free energy/information (entropy) of system as the system maturates and declines. This is contrasted to the current perspective that focuses on the processes of system optimization at different stages of development. The current study offers new insight as to the connection between the two theories and is discussed in section 5.

Because “structure” is a core concept to the developmental cycle, clarification is needed as to what is meant by it (for a complete discussion see (Johnson 2002) and (Bedau et al 1998)). In the above introduction, structure is used to describe sustained features of a system that direct the evolution of the system, as might be captured by genetic adaptations or social institutions. In the analysis herein, “structure” is used to describe the collective pheromone paths, which require reinforcement to be sustained. Both usages of “structure” convey how evolved features affect the future dynamics of the system by limiting the exploration of the potential options of the system. The difference between the two types of structure, internally versus collectively sustained, is important when environmental change is considered (discussed in section 5). In the following discussion, they are considered equivalent.

Figure 2 summarizes the three stages, as they would apply to the current system. There are many ways that the developmental process can be viewed: 1) the origins of system-wide function or performance, 2) the roles of diversity in the dynamics of the system or 3) the chaotic nature (or not) at the local and global levels of the system. Each of these represents different perspectives of the same underlying processes, but they are often treated separately in the absence of a unifying viewpoint. Each of these viewpoints is discussed in turn.


Time = 50 time units

Time = 150 time units

Time = 1000 time units




Formative Stage

Co-Operational Stage

Condensed Stage

·  Locally chaotic (agent’s path)

·  Globally chaotic (productivity)

·  Low and evolving “structure” – no collective network

·  Performance due to uncorrelated diverse contributions

·  Production by “innovative” agents

·  High (spatial) diversity

·  Robust to change

·  Locally chaotic

·  Globally predictable

·  Adaptive “structure” – robust collective network

·  Performance from combination of diverse contributions

·  Production by both classes of agents

·  High diversity

·  Robust to change

·  Locally predictable

·  Globally predictable

·  Unchanging “structure” – dominant collective network

·  Performance due to optimized population (low diversity)

·  Production by collective agents

·  Low diversity

·  Not robust to change

Figure 2: The stages of development for a stable environment, illustrated by the states of the simulation at
different times for a stationary source.


The system performance, in this case defined to be the rate of production, is initially from individuals that happen to randomly locate the source. The random discovery of the source is the most decentralized (uncoordinated) stage of the system and illustrates the initial vague or completely undefined state of the system.

Because the agents do not genetically evolve or remember complex past states or have finite lifetimes, the formation of individual definition (features or capabilities of agents) by selective processes (i.e., natural selection) in the Formative stage (Johnson 2002) is not as richly expressed in this example as in other systems. In the current system, the creation of definition is limited to individuals using their limited memory of what direction they are going to optimize there own food gathering. For example, because the agents in the current simulations reverse their direction after leaving food at the nest, during the initial exploration of the system the population becomes more “defined” as individuals are “self-selected” to concentrate on the sector where the source resides. This self-selection of direction is independent of the effect of the pheromones trails and causes an improvement in the production rate in the Formative stage, above the simple random discovery of the source. This increased production can be observed by comparing two simulations with a high evaporation rate (i.e., no collective effects are possible): one simulation with a 180 degree reversal in direction after depositing food at the nest and the other with a random selection of direction after depositing food; the simulation with the 180 degree reversal shows the higher productivity associated with the Formative stage.

The further development of the system beyond the Formative state depends on the details of the specific simulation. For example, if the population is sufficiently dispersed (low spatial density) or the source sufficiently distant, the individual pheromone trails are not sustained for collective use and only independent individuals contribute to the productivity (as in the first graphic in Figure 2). Under these conditions, the system will remain in the Formative stage.

If there is a sufficient spatial concentration of agents and the source is sufficiently near to the nest, the individual pheromone trails will begin to self-assemble, creating a collective structure. This is the beginning of the Co-Operational stage. The choice of “Co-Operational” is meant to capture the processes by which agents solving individual problems, primarily on their own, but on a common problem domain, contribute to higher collective performance (Johnson, 2000). The Co-Operational stage is the transition between the system productivity dominated by individual performance to being dominated by collective performance. For the current simulations, the higher performance in the Co-Operational stage is the increased efficiency of food collection when the individuals use the collective structure to optimally locate the source, rather than using a random search. This collective assistance occurs either when agents within the collective drop off food at the nest and are then guided to the source or when individuals randomly encounter the collective structure and then are guided to the source.

The higher performance by the collective in the Co-Operational stage can also be expressed as the collective “discovery” of the shortest path between the source and the nest and as the exploitation of the food sources that are closest to the nest. Both of these lead to higher rates of productivity and are the expression of emergent properties of the system – a global property that cannot be determined from knowing the properties of the individuals alone. Note that in the Co-Operational stage, there is productivity both from the collective agents and from the individuals. In the Condensed stage, most of the agents are part of the collective, and the system performance is maximized and resides primarily in the collective production. Figure 3 quantifies the above observations for the simulation illustrated in Figure 2. Note that because of the random nature of the simulations, identical results to Figures 2 or 3 may not be duplicated, but averaged quantities should be similar.

 The upper graphic in Figure 3 shows the total food production of the system. Note the intervals between 100–150, 150–400, and 500–1000 time units have approximately constant slopes with values of 0.14, 0.43, 1.01 food units/time unit. These correspond to the three stages described above. (The slope in the Formative stage is extremely variable and the value given above is for a run with a high rate of evaporation, which prevents the transition to the Co-Operational stage and yields a reproducible value.)

 The increased production in each subsequent stage corresponds to the increased influence of the collective structure, as noted above. Because of the chaotic nature of the agents, the regions of transition between the stages will vary from run to run. For example, a run using different seeds for the random number generators (which determine random choices of direction for the agents) result in different curves and different transitions for the three stages. Because the environment is fixed in this simulation and because of the parameter choices for the evaporation and diffusion rate, there is a strong drive for the system to develop to the Condensed state, and no variations from run-to-run were observed to prevent the system from achieving the Condensed state. As noted earlier, operating parameters and initial conditions can be chosen (e.g., the evaporation rate or the source location) which can shift the transition regions or stall the developmental process entirely, even for a constant environment.

Figure 3: Plots of various measures versus time in the approach to steady state by the system. “Collective” refers to the agents that are actively engaged in production within the collective pheromone structure (see text). “Individuals” refer to the agents that are not part of the collective. The “collective age” is the average time that an agent in the collective is continuously productive. The “structural efficiency” is a measure of the efficiency of the collective structure; a negative value means the collective structure is inhibiting the global system performance.

All the measures in Figure 3, except the plot of the total food, require the definition of a collective defined in section 2 (agents that have found food and thereafter are continuously in the collective structure). Being “in” the collective structure is defined as being within a sustained pheromone strength greater than 0.05 concentration units (similar to the extent of the pheromone clouds plotted in Figures 1 and 2). Any agents not in the collective are individuals or innovators. Note that an individual can be in the collective structure, but not part of the collective until it finds the source. Hence, this definition of collective is based on productivity, not just the sharing of information. An alternative definition, and possibly more commonly made, is based on agents within the collective structure. Within a stable environment, both of these measures would yield similar results, but in a changing environment, the definition herein focuses on the critical aspect of the system, the productivity.

Given this definition of the collective, a variety of measures can be defined, as illustrated in Figure 3. For example, the number of agents in the collective can be divided by the total population (100) —this is plotted as the fraction of the population in the collective. Similarly, the rate of production (of food) by the agents in the collective can be divided by the total rate of production — this is the fraction of production by the collective. (Note that the fraction of production by the individuals not in the collective is just unity minus the fraction of production by the collective.) Another useful measure is based on the duration that an agent spends continuously in a collective. The average of this duration or “age” of the collective is plotted as the Collective age. Note that the collective age does not continually increase in the Condensed stage, because of the failure of the agents to stay in the collective structure due to errors in tracking the pheromone gradient. Hence, even in a stable Condensed stage, one third of the agents on average are outside of the collective.

The final collective measure, the structural efficiency, is a direct measure of the efficacy of the collective structure in the system productivity. The structural efficiency is found from the difference between the fraction of food collected by the collective and the fraction of the population that is in the collective (the difference of the two upper curves in the bottom graphic in Figure 3). It is bounded between unity and minus the faction of the population in the collective.

A more intuitive, but equivalent, way to define the structural efficiency is as the difference of two production rates (and then divided by a total production rate to make it dimensionless): the actual rate of production from the collective minus the rate of production from the collective if the collective effect was neutral (i.e., production just by numbers). Hence, the measure represents the additional efficiency of the collective. By multiplying the structural efficiency by the total production rate, the added production rate of the collective can be found. The creation of a measure that focuses on the effect beyond a neutral process parallels the approach taken by Bedau in defining lasting adaptations beyond neutral adaptations (Bedau et al 1998).

For example, if the structural efficiency is zero, then the production by the collective and individuals are proportional to their relative numbers. If the collective production is higher than predicted from its proportional representation, then the structural efficiency is positive. If the production by the individuals is relatively higher, then the structural efficiency is negative. Because the structural efficiency is calculated from the difference of two noisy, largely uncorrelated numbers, it exhibits large fluctuations as observed in Figure 3 (for a longer average from 1000 to 5000 time units, the structural efficiency for the base simulation is 0.14 with a standard deviation of 0.08). Note that the decline in the structural efficiency in Figure 3 is anomalous to this run.


Many different measures can be defined for diversity in a decentralized agent system, from local to global measures (see (Johnson 2000 and 2002) for a more complete analysis of the different types of diversity and their influence on self-organization). In the current simulations, all of the agents have the identical rule set (the three rules listed in section 2) and minimal memory (the current direction of movement and “do I have food or not?”), so local diversity measures that are based on agent differences are limited and are largely captured by the distinction between the collective and individual. A more useful measure is the global or domain diversity, captured by the spatial location of the agents in general, and by the sector location in specific. It is easy to see how this domain diversity might become important if the sources were randomly placed in the domain as a variation of the current simulation. An agent population with low domain diversity will be slow to discover the new sources. The correlation between discovery and domain diversity is a feature of the Formative stage, similar to the arguments used for selection from genetic diversity in genetic algorithms (Fogel 1999).

The selection from a variety of individual “discoveries” is part of the process of creating definition in the system, as described in the previous subsection. In the current simulation, the agents increase their definition by reversing their direction after dropping off food. This increased definition of the agents reduces domain diversity, but also increases the likelihood of the Co-Operational stage to form, by increasing the density of agents in the vicinity of the source. The general observation is that for the global system to improve its performance in the Formative stage – as based on the average individual performance, diversity is consumed. At any time in the process of selection, if diversity is not consumed or is regenerated, such as using a random selection of directions after dropping off food, then the system performance will be degraded by the presence of individual performers with lower productivity.

In the Co-Operational stage, just the opposite dependency on diversity is generally observed: diversity is required for the current system performance, and not as an investment for future system performance as in the Formative stage. For example, in the emergent discovery of a shorter path to a source (emergent because no individual has a global perspective), it is the combination of many diverse paths that leads to the shortest path (Johnson 2000), not the selection of one optimal path of a single individual from a diversity of paths. For this synergy of diverse individual contributions to be optimally expressed, the domain must have features that limit options beyond those that exist in the present simulations. One way to introduce such features is to put obstacles in the domain. Because other studies (Johnson 2000; Johnson 2002) focus on this feature of the Co-Operational stage and because the emphasis here is on the simplest model for studying environmental change, the added complication associated with a rich expression of this feature is not introduced.

In the Condensed stage, the collective structure strongly reduces the domain diversity of the population, as illustrated in Figure 2 — most of the agents are located in one sector of the system. The strong coherence, and hence the low diversity of the agents, is the signature of the Condensed stage.

In summary, for the current simulation with a fixed source of infinite supply, the domain diversity is reduced in the Formative stage, relatively unchanged in the Co-Operational stage, and is low for the Condensed stage. The changes of the system productivity presented in the last section can be viewed from the perspective of the interaction of the domain diversity in the system with different processes in each stage: the process of selection from diversity in the Formative stage, the synergistic combination of diversity in the Co-Operational stage, and the optimization in the Condensed stage.


One of the more illuminating views is the degree of randomness or predictability exhibited by different levels (local or global) of the system in the different stages. The initial vague state where the production is from random discovery of the source is the most chaotic state, both in the movements of the agents (local) and in the system productivity (global).

The Formative and Co-Operational stages when viewed locally at the agent level, both have a significant chaotic nature, expressed by the individuals that are not part of the collective and expressed by the increased chaotic movement of agents during the formation of the collective structure (this can be demonstrated by reducing the sensitivity of the agents to the pheromones or reducing the total number of agent, both which prohibit the Condensed stage from forming).

In the Condensed stage, most of the agents are captured by the collective structure and are therefore predictable. While not quantified, these observations can be made from an animation of the spatial placement of the agents, as in Figure 2. The difference in coherence in the collective structure between the Co-Operational stage and Condensed stage is captured quantitatively by the change in the collective age in Figure 3. While not shown, the standard deviation around the mean collective age is much greater for the Co-Operational stage than for the Condensed stage.

From a system or global view of production, the Formative stage is highly irregular - dependent on the random actions of uncoordinated individuals. But the productivity in the Co-Operational and Condensed stages is predictable (illustrated by the fixed slope of the total food curve in Figure 3), because of the stabilizing effect of the collective structure in a stable environment. This leads to the observation that the developmental cycle can be viewed as an interplay between evolving structure and randomness around this structure. This is also argued by Salthe in his developmental theory (Salthe 1993a).

In this section, the development of a simulation of foraging through progressive stages is examined for a stable environment from three viewpoints: the processes of productivity, the changes in diversity and the degree of chaotic behavior at local and global levels. The developmental perspective unifies these three viewpoints, describing how different processes, each with different degrees of chaotic behavior at the local and global levels, can operate on different degrees of diversity to yield various rates of productivity.


Given the above characterization of the model system in a stable environment, the effect of a changing environment on the system is now examined. The integration of these results with the developmental perspective is presented in section 5.

There are many ways that the system described in Figure 1 can be modified to capture the idea of environmental change. The one chosen is illustrated in Figure 4, in which the source is moved in a circle of constant radius at a fixed angular velocity from the beginning of the simulation (i.e., there is no establishment of a stationary state before rotation begins). By imposing different angular velocities, different rates of environmental change can be examined.


Figure 4: Implementation of the environmental change. The unlimited source from Figure 1 is moved in a circle with a fixed angular velocity. Note that no food is left behind as the source moves.

The simplicity of the presentation of this implementation and its ease in testing in an actual foraging experiment are the main reasons for its choice over alternatives. In fact, different alternatives were tried (such as gradually increasing the rotation rate of the source, starting and stopping the source, or making the source travel on an ellipse instead of a circle), but the choice in Figure 4 expressed most of the observed results.


The rates of production for the different angular velocities (rate of environmental change) are shown in Figure 5, along with a breakdown between the collective and individual contributions and with the standard deviation of the total production of the underlying data. Note that the results are reported for the time interval from 1000–5000 time units, after the initial transients have passed. These results represent a stationary state of the simulations, but not necessarily steady state behavior (i.e., the system may exhibit repeated transient processes that prohibit the approach to a fixed steady state).

To give some perspective of the magnitude of the rates used, at a value of 0.8 degrees/time unit (8 on the figure), the source at the specified radius moves at about half the speed as an agent traveling in a straight line (1 step/time unit).

Three major observations are made based on Figure 5:

·        The system productivity drops as the rate of environmental change increases.

·        The decline in the contribution from the collective is the major cause of the drop in

·        The variation (standard deviation) in the productivity increases significantly between 0.1 and 0.3 degree/time unit, suggesting a change of stability in the simulations.


Figure 5: Rate of production for different rates of environmental change from an average over 1000-5000 time units, broken down into contributions from the collective and individuals. The bars represent two standard deviations from top to bottom of the underlying data.

The animations for the simulations[4] that are the basis of the results in Figure 5 provide an understanding for above observations. As the environmental rate increases, the following progression is observed:

·        For the environmental rates from 0.0 to 0.1 degree/time unit, there is little change in the dynamics or productivity – the collective structure forms and exhibits no significant difficultly adjusting spatially to the moving source.

·        For the environmental rates from 0.1 to 0.2 degree/time unit, while the system as a whole retains most of its productivity, the individuals or innovators become essential for sustaining the collective structure in the region of the moving source. The reoccurring pattern is that the collective can quickly exploit the new location of the source in great numbers once an “innovator” finds the new location. The collective never contributes to finding the new location of the source. The pheromone trail of the innovator is the only connection between the misplaced swarm and the new location of the source.

·        At the environmental rate around 0.3 degree/time unit, the system expresses boom-and-bust cycles (so named because of the resemblance to stock market cycles). The collective structure experiences periodic collapse when it cannot continually track the moving source, and consequently the production rate has a much greater standard deviation.

·        At the environmental rates beyond 0.3 degree/time unit, the collective structures that continually form are not located near the current source; hence, the exploitation of the collective structure rarely occurs.

Note that the above observations at the extremes of environmental change (small and large) are not path dependent – simulations that let the system come to a “steady” state (the Condensed stage) before the source is moved result in production rates similar to those in Figure 5.

For three of the rates of environmental change (0, 0.2, 0.8 degree/time unit), the transient values of the production rate are presented in Figure 6. (The averaged values in Figure 5 are for the time interval from 1000-5000 time units of this figure. Note that the cumulative integral of the “rate = 0” curve over time is the total food curve in Figure 3.) Note that the two extreme values of the environmental change exhibit less fluctuation, compared to the intermediate value of 0.2 degree/time unit. The intermediate rate is rarely as productive as the simulations with a stable environment, but neither does it degrade to the low productivity of the simulations for the most rapidly changing environment. It does exhibit one “bust” at a time of 1000 time units. For the rate of 0.1 degrees/time unit, the system never was observed to exhibit a total collapse of the collective structure.


One of the detrimental aspects of the real world boom-and-bust cycle is not only the lower productivity, but also the loss of resources during the bust phase – if the same (or even reduced) productivity can be achieved without the “bust,” many of our systems would likely fair better. Although the resemblance of the current simulations to, say, the stock market is limited, there may be insights into the processes surrounding the boom-and-bust cycles in the current system that are applicable elsewhere – particularly in the role of the collective structure.

Figure 6: Time-resolved rate of production for three different rates of environmental change (the rates have units of degree/time unit).

Plotted in a separate figure (Figure 7), but with the same coordinate ranges at Figure 6, are the time-resolved production rates for the rate of environmental change that exhibits the greatest fluctuations (0.3 degree/time unit) – the system that is continually in a boom and bust cycle. This suggests the following study to better understand the source of the boom-and-bust behavior.

If the simulation represented in Figure 7 is redone with double the evaporation rate (8% instead of 4%), the number of “bust” events are reduced by a third (defined to be the number of excursions of the production rate below 0.2 food unit/time unit) and the total production is 20% higher. Effectively the higher evaporation rate reduces the fraction of the population in the collective structure by 20% overall, thereby freeing up innovators that can keep the collective structure from collapsing. Hence, the overall efficiency is increased even though it results in having fewer agents in the collective structure, because of the importance of finding the new location of the source. This implies that in times of change it is essential to allocate properly resources between the collective structure and innovators – with a greater emphasis on innovation with larger rates of change. As noted above, this is particularly true if the system of interest has disadvantages associated with bust phases, such as the loss of resources.

Figure 7: Time-resolved rate of production for the rate of environmental change of 0.3 degree/time unit.

As observed earlier for the unchanging environment, there is a positive feedback process by which a successful collective structure enlists individuals, due to the strength and extent of the collective structure. In the transient process of a boom-and-bust cycle, it is observed to be a trend (but not always) that the bust is preceded by a higher fraction of the production coming from the collective. This suggests that the bust is indeed proceeded by a “boom,” which thereby captures resources (innovators) that are needed to prevent a subsequent bust. The bust then frees up these resources and the new location of the source is found and tracked by the additional innovators. The new source is then exploited by the collective, and a boom begins. The success of this boom then repeats the cycle.

The above interpretation is also supported by the path dependence of the system for intermediate rates of environmental change. If the movement of the source is started after the system has fully developed the Condensed state, it is observed that the inevitable “bust” is more likely to occur sooner, and be deeper and longer. This observation supports the hypothesis that the higher coherence of the collective structure is the possible cause of the subsequent ”bust”.

For completeness is should be noted that this positive feedback loop in the boom-and-bust cycle is dependent on a fixed number of agents, which then must be allocated between two essential tasks (exploitation and innovation). The balance between exploitation and innovation has also been argued to occur for genetic algorithms (Goldberg 1998). In the current simulations, if unlimited resources, or at least additional resources were available, then the boom-and-bust cycle may not occur.


In the analysis of the system in a constant environment, the collective was found to increase the productivity. Given a changing environment, the question arises if this increased efficiency is retained or negated, or is there in fact an inefficiency associated with the collective structure?

The quantitative measure of the structural efficiency was introduced in §3 and was defined to be the difference of two fractions: the fraction of productivity provided by the collective and the fraction of the population in the collective. In Figs. 8 and 9 the changes of these fractions are present for the different rates of environmental change. A comparison of the two figures show that for low rates of change, two-thirds of the population in the collective (Figure 8) are providing over 80% of the total productivity. This increased efficiency of the collective declines in the transitional region of the boom-and-bust cycle, until the collectives are actually producing less than their proportionate representation.

These observations can be captured in plot of the structural efficiency (Figure 10), which dramatically illustrates the change in efficacy of the collective structure. At low rates of environmental change, the increased efficiency of the collective structure is at its maximum. It then declines and becomes negative as the collective ties up resources that are best utilized as innovators. To quantify this inefficiency, a simulation was done with a high evaporation rate (75%), which inhibited the formation of the collective, for a rate of environmental change of 0.8 degree/time unit and then compared to the simulation with the collective forming. The total production was found to be degraded by 50% in the simulation with the collective structure. This strongly illustrates that the collective structure is a major source of inefficiency for the system as a whole when the environment changes quickly.

Figure 8: The fraction of the population in the collective for different rates of environmental change from an average over 1000-5000 time units. The bars represent two standard deviations from top to bottom of the underlying data.

Figure 9: The fraction of production from collective for different rates of environmental change from an average over 1000-5000 time units. The bars represent two standard deviations from top to bottom of the underlying data.

Figure 10: The structural efficiency (positive when the collective structure increases the system productivity) for different rates of environmental change from an average over 1000-5000 time units. The bars represent two standard deviations from top to bottom of the underlying data.

Figure 10 also shows the sensitivity of the standard deviation of the structural efficiency in the transitional region, more so than any other variable previously examined. This sensitivity is amplified by plotting the coefficient of variability (Figure 11) for the values in Figure 10. This plot dramatically indicates the extreme instabilities and changing processes in the transitional region. The coefficient of variability is normally a general indicator of population variability and is measure of potential that selective processes can operate on a population. The interpretation here is that the collective structure of system is expressing the maximum variation (from the extremes of Formative to Condensed) and the system is in an optimal state to select from this “population.” This suggests that if the environmental rate quickly increased or decreased in this region of maximum variation, the system would quickly select the optimal state for the new rate.

Figure 11: The coefficient of variability (ratio of the mean to standard deviation) of the structural efficiency for the data in Figure 10.

Figure 12 is a comparison of the time-resolved structural efficiency for the two extreme rates of environmental change (0.0 and 0.8 degree/time unit) and the transitional environmental rate (0.3 degree/time unit). Not only does the time-resolved structural efficiency illustrate the boom and bust cycle in the transitional region, it also momentarily exceeds the values of the collective performance and failure of the system in the stable and rapidly changing environments, respectively. This supports the conclusion that the boom-and-bust state expresses features that do not exist during a stable environment. The reason for this extremely high and low collective productivity is not known, but an examination of the animation suggests that it may be due to the periodic boundaries that can create coherent waves of agents in the system after a bust event. These waves may contribute to extremes of resource allocation that do not occur in the other simulations.


In section 3, a developmental theory (Johnson 2002) was applied to a simulation of ant foraging with a stationary source (summarized in Figs. 2 and 3) from three perspectives: the source of system performance (by individuals, by synergistic combination of individuals and collective, and by an optimized collective), the role of diversity in system performance (selective, synergistic, excluded), and chaotic aspects of local and global levels of the system. Then in section 4, the behavior of the same system for different environmental rates was examined, focusing on three aspects of the system: changes in productivity, the boom-and-bust transition, and the efficiency (or inefficiency) of the collective structure. This section integrates the developmental theory with the results from studies of the changing environments. To aid in the discussion, the designations of the ranges of each developmental stage are indicated at the right of Figure 5 and are based on the productivity levels observed in the simulation with no environmental change.

Figure 12: Time-resolved structural efficiency for three rates of environmental change.

The foundation of a developmental view of self-organizing systems is that there are internal and system processes that guide the system through stages of development. The developmental path can be expressed as an interplay of growing structures (coherence) in the system and variations around these structures (Salthe 2000). In the current example, the structure that guides the development of the system is the collective pheromones. Because there are no global mechanisms or rules that select or ensure the continued existence of the collective structure, it is a self-generating (emergent) structure of the system that must be continually reinforced. In a stable environment with an infinite source, the attractor nature of this structure is strong, and the system will, under most conditions (parameters and initial conditions), be dominated by this structure.

With the addition of environmental change, the same system (identical internal rules) can express either the same developmental path, no development at all, or wild global fluctuations that have little resemblance to the system in the stable environment (see Figure 12). For the extremes of environmental change (small and large), the animations assist in associating some of the different rates of environmental change with a specific developmental stage. At lower rates of environmental change, the system develops “normally” and then retains the features of the Condensed stage. At the high rates of environmental change, the system exhibits the features of the Formative stage with little collective structure and with production only from individuals.

The suggestion from the above comparison of the system with and without environmental change is that the addition of environmental change inhibits the self-generated developmental progression or, in the case of rapid introduction of environmental change onto a developed system, causes the system to revert to an earlier stage. This suggestion implies that there is a correspondence between the effect of an internal state (e.g., parameter choices) and external factors, because a similar system response can be obtained from either internal or external changes. For example, the collapse of an evolved collective structure in a stable environment can be induced from an internal change, for example, by decreasing the sensitivity of the agent to the pheromone concentration. The possible correspondence between the internal and external effects could lead to a more general treatment of developing systems and could explain why studies that do not explicitly examine the effect of environmental change may still apply to systems that do experience changing environments.

The analysis of the inefficiencies of the collective structure shows how the same collective processes can lead to quite different global effects in the absence or presence of environmental change. In the current system, the collective structure for a stable environment is never observed to cause a drop in efficiency of the system as a whole. This collective inefficiency was only observed in the simulations with moderate to large rates of environmental change. What implications do these observations have for other

In most real systems, there are mechanisms for sustaining evolved structure, for example by genetic means in biological systems or by laws or regulations in social systems. The current foraging simulations do not contain any mechanisms for sustaining the collective structure (except by the weak reinforcement associated with the reversal of direction upon leaving the nest). But given the behavior of the system with slowly changing collective structures in fast changing environments, the effect of sustained collective structures in the current system is hypothesized to increase the sensitivity to environmental change, which would result in reduced productivity even at lower rates of change.

This increased sensitivity and consequent decline of productivity can be easily captured in the present simulations by retaining the pheromones of the collective above a certain concentration level (25 concentration units for the simulation in Figure 13 – chosen to retain only the strongest collective structures). In the presence of no environmental change, this modification has no effect. But, in the presence of even a small environmental change (see Figure 13), the result is a continual creation of collective structures that over time do not lead to any source. (Although not shown, the structural efficiency for this simulation starts out small, reaches a maximum and stays constant; it then declines sharply and becomes negative.) The accumulation of these structures results in almost all of the resources being captured by the collective and a complete loss of productivity when there are no longer innovators to locate the new position of the source. This is the identical process observed in the boom-and-bust cycle, except expanded over a much longer period of time.

Although this simulation of sustained structure represents a worst case for the effect of sustained structure (because in most systems some production must continue for the system to remain viable), it does illustrate that features in realistic systems that capture lasting structure may increase the sensitivity to even slight environmental changes. This observation illustrates the importance of considering the effect of sustained collective structures in realistic systems in the presence of environmental change and suggests similar studies with other systems would yield further insights.

The above results suggest the following generalization. In systems that create collective structure and sustain it through some internal mechanism, the interaction of the growing accumulation of initially efficiency-producing structures in the presence of even minimal environmental change will cause growing inefficiencies in the system as a whole. Ultimately as the system develops in this gradually changing environment, the efficiency of the system as a whole will continue to decline. This scenario duplicates Salthe’s description of the decline in efficiency of a senescing system, but without the need of invoking greater and greater creation of detailed structure (argued by Salthe to be the source of the decline of the system). The above connections between environmental change and sustained structure appear to be the bridge between the developmental theories of Salthe and Johnson.

Figure 13: Plot of the total production for an environmental rate of change of 0.1 degrees/time unit for a transient collective (similar to the simulation for in Figure 3) and for a system that creates sustained structure (see text). The time of 3000 time units is approximately one revolution of the source.

Models of self-organizing systems by their very nature can contain surprises that their creators never expected. It is for this reason that self-organizing models can be explanatory of global behavior, rather than descriptive of observed behavior in models that are not-self-organizing (Hemelrijk 1997). An example of an unexpected result in the current simulations is that a region of high productivity was found at much higher environmental rates (5-7 degrees/time unit or 10 times faster than shown in previous figures). In fact, the total productivity and stability was comparable to the system with low rates of environmental change in Figure 5. This high productivity regime occurs because of the resonance between the time it takes for an agent to make a round trip between the food and the nest and the time it takes for the source to return to the same location (or integer fractions of this time).

As an indication of how different this regime is from the others described previously, the highest production rates of the innovators are observed occur in this region of any simulation – as much as double those shown in Figure 5. The total production rates are relatively flat through this region because of the loss of production from the collective is balanced by the increase production from the innovators. No physical analogy is known for this highly productive regime, but this example illustrates how collective models can perform in ways never envisioned by the modeler.


The theme of this paper is the application of a developmental perspective to a system with different rates of environmental change. This perspective is largely missing in the earlier studies by Salthe (Salthe 1993a) and Johnson (Johnson 2000). In this paper, the dynamics of a simulation of a well-studied system (ants foraging for food) under different rates of environmental change is presented from a developmental viewpoint of self-organizing systems. The richness of the results suggest that complex models are not required to capture much of the complexity we observe in real self-organizing systems (e.g., ecosystems, financial markets, political systems) – a common conclusion of agent models in general. Secondly, much of the richness of the observed behavior originates from the interplay between structure and dynamics of the system. This suggests that in a complex system with stabilizing and transient components, being able to differentiate between these components and their contributions to the system as a whole may prove enlightening. The proposed metric for the structural efficiency may assist in these studies. Finally, the use of a developmental perspective for stable environments to the response of a system undergoing environmental change provides guidance on the interpretation of the observed features.

Guided by a developmental perspective, a measure is proposed that is proportional to the efficacy of the collective structure – the structural efficiency. This measure is shown to be positive for zero or low rates of environmental change – indicating that the collective structure adds to the overall efficiency of the system. For high rates of environmental change, the average structural efficiency is negative, indicating the collective structure actually lowers the efficiency of the system. It is also shown that by reducing the strength or coherence of the collective structure, the system as a whole under extreme environmental change can increase its productivity as much as 50 percent. This is a clear indication of the possible negative consequences of a collective structure in changing environments.

Between the two extremes of dominant productivity from the collective and individual contributors (corresponding to low and high rates of environmental change), the system is observed to go through a transitional region where the structural efficiency oscillates between large positive and negative values, which correspond to large swings in system productivity (a boom-and-bust cycle). An analysis of the transitional state before a “bust” suggests that the cause of the bust is an excessively strong and effective collective structure that captures free resources (innovators) – the specific resources that are needed to retain the higher efficiency of the collective structure in changing environments. A positive feedback loop – where a successful collective engages more resources which makes the collective more successful which engages more resources – combined with the loss of innovators is the origin of the boom-and-bust cycle. It is shown that by decreasing the strength of the collective structure, the system avoids the boom-and-bust cycle at the same rate of environmental change, and productivity is greatly enhanced.

The quantified inefficiency of the collective was also observed in a different but related circumstance. By the introduction of sustained structure in the simulations, the system became more sensitive to environmental change and the equivalent of the “bust” phase occurred at even small rates of environmental change. This suggested that in systems with mechanisms for sustained collective structure as might be captured by genetic adaptations in biological systems or by regulations in social systems, the detrimental effect on the system of even subtle environmental change should be considered. The failure of large corporations to adapt to changing times is a contemporary example (Foster and Kaplan 2001).

If the simple model system is similar to human organizations or, in general, managed systems, the analysis of the boom-and-bust transition region suggests that the division of resources between exploiting the collective structure and innovation must be carefully managed in the presence of increased environmental change. One way to view the challenge of balancing resources between the innovators and the collective is managing diversity (as capabilities or sources of information). The collective structure reduces diversity (in the current example, diversity is the variety in agents’ location throughout the domain). The strategy of increasing the effectiveness of the collective by devoting more resources to the collective structure may indeed increase the productivity momentarily, but, in the presence of environmental change, it reduces the robustness of the system. In the real systems, this diversity management can be expressed in many ways, from managing diverse information sources to diverse approaches to solving problems.

In summary, the simple system examined provides a testbed for the development of new approaches to understanding and modeling systems undergoing environmental change – a significant area of interest in our fast changing world. Furthermore, the analysis of this simple system suggests new measures (e.g., the structural efficiency) that may be applicable to real world systems undergoing change in order to better understand the beneficial and detrimental roles of collective structures.

Future studies of this system might consider other implementations of environmental change to explore alternative adaptive capabilities of the system. In addition, the agents themselves can be given internal attributes (instead of being identical), which would increase the correspondence to real systems. For example, the agents could use different strategies to accomplish the same goal. Additionally, in the current analysis the population was divided into only two groups, agents in the collective and agents as individuals. A fruitful extension might be to subdivide the individuals into satellite individuals that are “free” but still near to the collective (e.g., those that escape the collective structure due to tracking failures or those individuals in the collective structure) and those individuals independent of collective structure. Possibly, these two subgroups of individuals contribute quite differently and would lead to a refinement of the current conclusions. Finally, the original developmental theory of self-organizing systems (Salthe 1993a; Johnson 2000) was proposed based on relatively stable environments. The current study suggests that an extension of the theory could be proposed to include the effect of inefficiencies in the collective structures in the presence of gradual environmental change – particularly the inclusion of inefficient collective structures that may not appear or be sustained under stable environments.


The author gratefully acknowledges insightful and continued assistance of Stanley Salthe. This research is supported by the Department of Energy under contract W-7405-ENG-36.


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