=Paper=
{{Paper
|id=Vol-2448/SSS19_Paper_Upload_230
|storemode=property
|title=Maintaining Knowledge Distribution System's Sustainability Using Common Value Auctions
|pdfUrl=https://ceur-ws.org/Vol-2448/SSS19_Paper_Upload_230.pdf
|volume=Vol-2448
|authors=Anas Al-Tirawi,Robert G. Reynolds
|dblpUrl=https://dblp.org/rec/conf/aaaiss/Al-TirawiR19
}}
==Maintaining Knowledge Distribution System's Sustainability Using Common Value Auctions==
https://ceur-ws.org/Vol-2448/SSS19_Paper_Upload_230.pdf
Maintaining Knowledge Distribution System's Sustainability Using
Common Value Auction
Anas Al-Tirawi
Computer Science Department, Wayne State University,
Detroit, MI 48202, U.S.A
aaltirawi@wayne.edu
Robert G. Reynolds
Computer Science Department, Wayne State University,
Detroit, MI 48202, U.S.A
reynolds@cs.wayne.edu
Abstract
In Cultural Systems there are many ways to collect and environment so that it can maintain or improve its
distribute problem solving knowledge within social performance over time [1].
networks. Such mechanisms include games, auctions, and A cultural system will devote some of its resources
various voting mechanisms. Here, a new auction to each of these two properties. If too many resources
mechanism, Common Value Auctions, is presented. In this are devoted to robustness in the short term, it may
paper Common Value Auctions are used to distribute impact its ability to be resilient in the long term and
problem solving knowledge within a given model of social vice versa. So there needs to be a balance between the
systems. These mechanisms are compared with the other two in order for a system to be sustainable over the
distribution mechanisms in the solution of dynamic real-
long term.
valued optimization problems. Specifically, their relative
abilities to support the robustness and resilience of Cultural One key aspect of a Cultural System is how
Systems in environments that vary in their dynamic information can be distributed throughout its social
complexity from static to chaotic are assessed. The Cultural networks in order to support both robustness and
Algorithms Toolkit (CAT) is used as a vehicle to generate resilience. In this paper, the impact that various
real-valued dynamic problem landscapes of varying knowledge distribution mechanisms in a system will
complexities. The results show that using the Common have on the systems robustness and resilience will be
Value Auction in CAT4 has significant improvements over
assessed. These mechanisms include voting schemes,
Weighted Voting methods (CAT2) in terms of both
auctions, games, and pure random processes. They
robustness and resilience across complexities that range
from static to chaotic. will be studied through the lens of a computational
model of cultural evolution, Cultural Algorithms.
Keywords—cultural algorithm, sustainability, evolutionary
algorithm, common value auction, robustness, resilience. In the next section the basic knowledge
distribution mechanisms currently available for
I. INTRODUCTION Cultural Algorithms are discussed. In section III the
new knowledge distribution mechanism, Common
Cultural systems provide a framework for human
Value Auction, is described. Section IV describes the
existence. One key observation that can be made is that
dynamic landscape in which the performance of the
certain cultures are more sustainable over time than
new mechanism will be assessed. Section V provides
others. Robustness and reliance are key factors behind
a description of the experimental framework through
the sustainability of cultural systems. These two
which the sustainability of the Cultural Algorithm
factors are needed, so the system can handle a wide
systems will be assessed. In the following section the
range of inputs/ perturbations while maintaining its
performance of Common Value Auctions will be
integrity, structure, and reducing the severity of the
assessed in terms of the systems relative sustainability.
impact that these perturbations can have on a system.
Section VII presents the conclusions and suggestions
Robustness is the property of a complex system to
for future work.
withstand the impact of a dynamic change or
perturbation in its environment. Like a boxer in the
ring, robustness is the quality of a system to endure a
series of blows but still continue to function at a certain
level or above. Resilience on the other hand is the
ability of the system to adapt to the dynamics of its
This work was supported by NSF grant #1744367.
II. KNOWLEDGE DISTRIBUTION in the population space. Fig. 2 shows the different
MECHANISMS IN CULTURAL knowledge distribution mechanisms. It is important to
ALGOGRITHMS note that the amount of information that a distribution
algorithm knows about a problem solution the
The Cultural Algorithm (CA) was introduced by
fidelity), increases from left to right [8].
Reynolds [2] as a computational model of Cultural
Systems and their Evolution. It has been applied to The first mechanism was called the Marginal
many practical applications since then, one of which Value Approach by Peng [9]. Every individual was
is: modeling the origins of agriculture in the valley of controlled or directed by one KS in each generation.
Oaxaca, Mexico [3]. In addition, CAs have been Peng in her approach [10], integrated the five KSs in
applied to concept learning [4], decision trees [5], the belief space into a single influence function as
software testing [6] and other hybrid approaches [7]. shown in Fig. 3. Peng used a random process based on
their relative performance to select a KS to influence
an individual. A KS roulette wheel, with proportional
areas, based on relative performance, allows for an
informed random selection process. However, Peng
did not account for the influence among neighbors of
a social network.
Fig. 1 Cultural Algorithm s framework [3]
As shown in Figure 1, the CA is a knowledge
intensive evolutionary framework. First, the
individuals in the population space are evaluated in
terms of their performance in a problem space. Next, Fig. 3. Integration of multiple KSs [10]
a subset of individuals is selected via the acceptance
function and their performance is uploaded into the Next, Ali and Reynolds [11], [12] developed a
Belief Space which is a network of Knowledge majority win approach. In their approach the influence
Sources (KS). After updating the Belief Space of the neighbors is considered when selecting a
network, the KS’s can direct the next generation of KS. The social fabric is the connection between the
individuals in the population space via the influence individuals in a population. A conflict resolution
function. The knowledge sources (KS) utilize a variety process allows individuals to select the KS by which
of distribution mechanisms in order to circulate their they are influenced, if their neighbors are influenced
influences among the individual agents in the by one or more different ones. First, each individual
population space. was assigned a direct influence based upon the relative
performance of the KSs using a roulette wheel as
suggested above by Peng. Next, Ali used a conflict
resolution strategy based on majority win in order to
calculate the controlling knowledge source for each
individual. They summed up the direct influence of the
adjacent individuals in the social fabric and those of its
current neighborhood. After that, the KS with the
majority of the votes won the influence over this
individual in that generation of the systems.
Another approach is the Weighted Majority Win
proposed by Che [8]. Che used the average fitness of
each KS to determine how much weight this KS
deserves in the vote. The key to determining the
Fig. 2. The big picture for all Knowledge Distribution Mechanisms weight for each KS is the average fitness value of the
that utilize the CA. individuals that have recently been influenced by the
Previous mechanisms have utilized CAs to knowledge source in the population. Figure 4 gives an
distribute the influence of the KS over the individuals example of the weighted voting process. The
individual, A0, has information about five competing As with the previous mechanisms the process for a
KSs. They are represented in the figure as follows: S: generation starts with the assignment of a knowledge
Situational, D: Domain, H: History, T: Topographical, source to each individual as their direct influence. The
N: Normative. AO: represents the individual. The next step is the selection of the bidders, those KS’s
number of votes for each KS is given as x (number of who will be participating in the auction, from the KSs
votes). For Situational it is x3, or 3 individuals have it list. The KS’s compete to influence each individual
as a direct influence. The weight along each arc is the (x). The algorithm currently only allows the
normalized relative performance for each KS. immediate adjacent neighbors of the individual (x) to
participate in the auction. The actual auction takes
place as shown in Fig. 5, where the system requests the
selected bidders to submit their bidding values. Each
selected KS will spin its correspondent wheel to get
the bidding value. Finally, the auction system will
determine the winner and assign the winner KS to
influence individual (x) It may take several iterations
to do so as shown in Fig. 5.
Fig. 4. Weighted Majority win in belief space through the social
network [8]
In Fig. 4, the winning KS is the Domain KS, even
though it does not have the most votes (votes=2).
However, (D) does have the greater weight, which is
the key factor in the weighted-majority win approach.
Che has used many network topologies in his system
including LBest, Square, Hexagon, Octagon, Hex-
decagon, and Gbest. In addition to that he has also
tested his system on different problem complexity
levels [13]. As a result, Che concluded that when the Fig 5. Conducting the Auction [14].
performance function is of higher fidelity, the
weighted approach can spread new information faster In the auction mechanisms above the bidders did not
through a population than the majority win approach. know anything about the properties of the individuals
When the signal strength of information about the upon which they were making bids. Those properties
problem becomes even stronger, the auction approach can be the location in the network, the number of
can be effectively employed to find a immediate neighbors, and the strength of their
solution. Kinnaird-Heether and Reynolds’ [14], [15] connections, what knowledge sources have influenced
embedded auction mechanisms into a CA. The new it in the past, among others. In the next section an
version was called CAT3. In CAT3, the production approach, the Common Value Auction, is discussed.
of the bidding tokens is the starting point. The process This approach provides a common set of parameters
starts by producing bidding tokens and uses them to that are available to all bidders. These parameters can
form biddings wheels for each KS. These tokens are be used to condition the bids made by the participants.
generated by listing all of the individuals that were
III. THE COMMON VALUE AUCTION
recently influenced by a KS over a given previous time
DISTRIBUTION MECHANISM
window, t.. The individual’s fitness values are the
bidding tokens and are normalized so that each The new mechanism, Common Value Auction
previous result takes up a relative proportion of the Toolkit (CAT 4) is an extension of CAT3. CAT4
token bidding wheel. The bid of a KS corresponds to propagates the influence using the Common Value
the performance associated with the result of spinning knowledge. The Common Value knowledge is a set of
the bidding wheel. Since the KS’s do not have specific parameters that every KS can know about the
common knowledge about the location of the individuals in the social network. These include the
individual in the population network, the process must individual’s location in the network and the KS(s) that
be stochastic based upon past performances.
influenced the individual in the past previous incentive for the KS that influenced the individual in
approaches. the past and for those KSs that were able to influence
the individual’s neighbors.
The first step is to build the KS wheel (one wheel
for all KSs), by normalizing the KS average score, In the fourth step, the influencers that satisfy their
where every KS will have a wheel’s share that reflects bidding rules are then chosen to participate in the
its average (score). Each KS will have a portion of the auction. The bidding wheel is spun for each to
wheel that reflects the average performance of those determine their bid. In the experiments conducted here
individuals who have been influenced by the each KS had a bidding wheel comprised of a single
correspondent KS. Next the algorithm assigns a direct average value for the performance of the selected
influencer KS randomly using the roulette wheel subset in order simplify computations at this stage.
approach discussed previously to each individual in
the population. This step is the same as that for all In the fifth step, the bidding strategy rules that are
other mechanisms discussed so far. satisfied for a KS are then applied to the bid as shown
in the rule above to give a final bid for that KS. The
In the second step each KS constructs a bidding bids are then compared with each other and the winner
strategy wheel that will be used later to determine their is selected to control the individual for that generation.
bidding decision on a specific individual. This is done If there are no bidders, then the direct influencer of the
by selecting a subset of recent individual performances individual is retained. This redistribution process is
directed by that KS over a given past time window. A then repeated for all individuals in the network.
wheel is constructed such that each score comprises an
area that is proportional to its contribution to the total Fig 6 covers the big picture of CAT4. First, the KS
score of the subset for the KS. In addition, a set of rules roulette wheel is spun to generate the direct influencer
is selected to determine whether the KS will bid on an for each individual. If one or more of the individual’s
individual based upon common value knowledge neighbors possess a different KS then each decides
about the location of the individual in the social fabric. whether it wishes to bid for that individual using the
common value information about individual. The
Next, the direct influence for each individual in the selection process is governed by a rule-based expert
population is compared against those of its neighbors, system associated with each KS. The selected KSs
here, just the directly adjacent neighbors are used. If then participate in the bidding for the auction as
the direct influence of an individual agrees with those described above.
of all of its neighbors, its direct influence is then
chosen to guide it during that generation. Otherwise an
auction is conducted between those KS’s who directly
influence that individual and its neighbors.
In order to do this the bidding strategy for each of
the competing KSs is checked to see if it will bid on
that individual based upon the common value
information. The rule set associated with the KS is
checked to see if taken together they support a bid on
the current individual. This “expert system” can
technically be comprised of many rules. For the
experiments here, the same one rule is used for all KSs.
To do so, the following distributing mechanism based
upon just one subset of common values, the extent to
which the individual and its neighbors have been
influence by the KS in the past:
If KS[j] has influenced individual (i) in the
past m generation or
If KS[j] is influencing currently the
neighbors of individual
Then increase the bidding value by a bonus
as shown in the equation below:
Bidding value= KS’s bidding value + 0.5 Fig. 6. Big picture of CAT4 Algorithms
(Boost) (1)
This is where the Common Value information is
used to determine the winner. We simply give
IV. THE DYNAMIC PERFORMANCE ENVIRONMENT: As the value of A increases, the system generates
THE CONES WORLD more complicated behavior. Figure 8 shows how Y
will change as a result of A for a sequence of
To analyze the results and test the performance on
landscapes. The x-axis gives the number of
the different levels of complexity, a robust problem
generations, the z axis gives the A value, and the Y
generator (Cones World) was used in both CAT2 and
axis gives the Y-value produced over the given
CAT4. The Cones world framework was inspired by
generations for a specific A. Each of the Y trajectories
the work of Morrison and De Jong [16]. This tool has
is color coded with the A value that produces it. The
the ability to generate dynamic problem environment
color code is in the legend on the right side of the
over various landscape complexities. A given cone
graph. Low values of A produce gradual linear
world configuration can be described as follows:
changes while high values produce wildly oscillating
2 values for Y. In the next section we discuss the
f(⟨x1 ,x2 ,…,xn ⟩)= max (Hj -Rj ∙√∑ni=1(xi -Cj,i ) ) (1) experimental framework of CAT4. Also, we explain
j=1,k
how the dynamic environment can affect the learning
Where: K: the number of the Cones. Hj: the cone curve of the whole system and consequently the
height, Rj: the cone slope, N: the dimensionality. Cj, i: produced results.
Coordinates of the cone j in dimension i., (Xi, Yi):
determine the location of the cones on the landscape.
The values for the cone height, slope, and coordinates
can be assigned randomly through the problem
generator or logistic function. However, the values
would be selected from the ranges below:
Hj ∈ (Hbase, Hbase +Hrange); Rj ∈ (Rbase, Rbase
+Rrange); and Cj,i ∈ (-1,1).
The Max function here is used to handle the
combination of the cones when they overlap. For
example, if two cones overlap, the Max function will
choose the height of the combined cone to be the
height of the highest cone for the two overlapped
cones. Fig 7 shows how the landscape looks like with
the following parameters: k = 15, Hbase = 1, Hrange
= 9, Rbase = 8, and Rrange = 12. To determine the
dynamic changes of the system Morrison and De Jong
used the logistics function below:
Yi = A ∗ Yi−1 ∗ (1 − Yi−1) (2)
A= Constant value, Yi = is value of Y at iteration i.
Fig. 8. The value for Y (on the Y-axis as a function of A (z axis) over
the number of generations, x axis. The Y curves are color coded with
A-value that generated them. The color code is on the right.
V. DYNAMIC EXPERIMENTAL FRAMEWORK
In these experiments, the performance of the
Common Value Auction was compared with the
Weighted Majority algorithm for three complexity
levels of A= {1.01, 3.35, and 3.99}. These three A-
Values were selected because they represented a wide
spectrum of complexities over which to test CAT4
against. The full list of experimental framework
parameters are summarized in the table below. The
Fig.7. an Example Landscape In two-dimensional space (n = 2) types of social fabrics are explained here [17].
bound by x ∈ (-1.0, 1.0), y ∈ (-1.0, 1.0) with k = 15, H ∈ (1, 20), and
R ∈ (8, 20) [9].
TABLE I. EXPERIMENTAL FRAMEWORK be used to affect bidding strategies, the focus here will
PARAMETERS be in just a single set of factors, the KS previously used
to influence an individual and its neighbors. The goal
Parameter Name Value
will be to show that the addition of just this new
Complexity Class 1.01, 3.35, and 3.99 information can make a substantial difference in the
performance of the cultural system.
Number of Runs Per 300
Complexity The first dynamic landscape to be assessed was
that produced by A=1.0. As seen in the previous
Number of landscapes 50 section, that landscape involves a series of small linear
Max. number of generation per 800
shifts in the locations of the cones. Fig. 9 gives the
landscape standard deviation of the two systems over all three
environments. For the linear dynamic landscape, the
Number of cones 100 two systems each was perturbed by an average of
around 85 generations for each landscape change. So
Number of agents 50
their relative level of robustness is about the same for
Social fabrics {L-Best, Square, Hexagon, this environment.
Octagon, Sixteengon, Global}
Max fitness value 20
CAT4 vs CAT2 Standard
Precision of solution 0.001
Deviation Comparison
100
The key hypothesis to be tested here is whether the
needed to find solutions for each landscapes
Common Value Auction mechanism is able to produce STD DEV of Average number of gernations 90
a more sustainable cultural system than the weighted
majority voting mechanism. The extent to which this 80
is accomplished will be observed in terms of the two 70
system’s relative robustness and resilience over the
course of 40,000 generations for each of the 300 runs 60
for the 3 complexity classes. 50
STD DEV of
CAT4
Robustness will be assessed in terms of the ability 40
of the system to bounce back after each of the 50 STD DEV of
landscape shifts for a given run. The standard 30 CAT2
deviation over the set of 300 runs will provide an
20
indicator of the need for each system to bounce back
from a landscape change. Resilience on the other hand 10
will be observed in terms of the extent to which the
systems are able to adapt to these landscape shifts by 0
reducing the time needed to achieve the optimum in
the next landscape. The systems will then be compared
in terms of how the complexity of the environment
impacts their relative sustainability as the Fig. 9 CAT4 vs CAT2 standard deviation comparison.
environmental complexity shifts from static, then to
cyclic, and finally to chaotic. The relative resilience of each of the two systems
in the linear landscape is illustrated in Fig. 10 and 11.
VI. A COMPARISON OF THE RELATIVE
SUSTAINABILITY OF THE COMMON VALUE AUCTION
Both systems are able to significantly reduce the
AND THE MAJORITY WIN KNOWLEDGE number of generations needed to find the new
DISTRIBUTION MECHANISMS optimum over time. The CAT4 system was able to
produce a correlation of (0.662) between the number
The main difference between the two algorithms is
that CAT4 uses information about the individuals of generations needed to solve the changed landscape
before the auction starts. The CAT4 algorithm is an and landscape number. The corresponding coefficient
informative algorithm that provides crucial of determination, the percentage of the total variance,
information for the bidders about past behavior of the explained by the correlation is (0.43). CAT2 exhibited
individuals in the population space. This information a coefficient of determination of (0.289). As shown in
is used to trigger bidding strategies for each of the Table II the correlations were significantly different
knowledge sources. While many different factors can
from each other at the (0.05) level of significance. So On the other hand, CAT4 improved on its ability
CAT4 was able to do a better job of adapting to the to adjust to the change in landscapes as reflected in an
changing linear environment than CAT2. improved correlation coefficient (0.73) and coefficient
of determination (54%) as shown in Table II. The
While CAT4 exhibited a significant level of Weighted Majority system exhibited a much lower
learning within an environment with linear dynamics, overall coefficient of determination, (0.17). Again, the
the next question is how it would adapt to an two systems exhibited a significant difference in
environment in which the changes were non-linear adaptability over time, but now in a nonlinear
from landscape to landscape. A nonlinear shift in cone environment.
location was produced by the landscape generated for Overall when the environment switched from a
A=3.35 as shown in Fig. 8 above. The relative change linear to a non-linear one the CAT4 mechanism
in robustness produced by the shift to a non-linear produced a distinctly more robust and resilient
dynamic for the two systems is given in Fig. 12 and behavior than CAT2. The next question is how the two
Fig.13. CAT4 exhibited an approximately 15 systems would adapt to an extremely “chaotic”
generation increase in terms of its response to a environment that was characterized by the
perturbation compared to CAT2. That is a significant superposition of numerous non-linear patterns of
difference in its ability to rebound from a perturbation behavior?
in this environment. On the one hand, a non-linear
environment required CAT4 to respond more robustly
than before. 500
Average Number of Generations Needed to Find the Optimum
500 450 y = -3.2381x + 243.66
R² = 0.2892
450 y = -3.7082x + 250.04 400
R² = 0.4382
Average Number of Generations Needed to Find the Optimum
400 350
300
350
250
300
200
250
150
200
100
150
50
100
0
0 10 20 30 40 50
50 Landscape Number
0 Fig. 11. CAT2 Regression line over 50 runs for complexity A = 1.0
0 10 20 30 40 50 Since the generating process was deterministic in
Landscape Nnmber nature, all of the information needed to provide a
perfect prediction of the environment’s dynamics is
there, it is just a matter of extracting all of the
Fig. 10. CAT4 Regression line over 50 runs for complexity A = 1.0 intertwined threads.
300
environment. While both system’s behavior is now
clearly nonlinear, the regression line provides a
general indicator of the additional stress that is placed
on each system over time.
In the first two environments, the systems were not
y = -3.3423x + 195.2 only able to survive the perturbations but to adapt to
250
R² = 0.5413 them. This produced a strong sense of sustainability.
Average Number of Generations Needed to Find the Optimum
Of the two, CAT4 was more able to exploit the
nonlinear environment. In the chaotic environment the
theme was less on adaptability but survivability over
time. Both systems displayed symptoms of stress over
200
time.
300
150 y = -1.0428x + 113.68
R² = 0.1706
250
Average Number of Generations Needed to Find the Optimum
100
200
50
150
0
0 10 20 30 40 50
Landscape Number
100
Fig. 12. CAT4 Regression line over 50 runs for complexity, A=3.35
As demonstrated in Fig. 14 and 15, the robustness
of the CAT4 system is still significantly greater than
that for CAT2. The difference in the number of 50
additional generations needed to response to a
perturbation is now 10. That is down from 15 before,
but still a significant difference in system robustness.
In such a chaotic environment learning is less of an
issue than sustainability. As shown in Table II the two 0
systems now exhibit a much lower level of resilience. 0 10 20 30 40 50
The coefficient of determination for CAT2 is now Landscape Number
significantly greater than that for CAT4 but notice that
the relation between the number of generations needed
to solve the problem is now increasing with increased
Fig. 13. CAT2 Regression line over 50 runs for complexity, A = 3.35
landscape number. The rate of increase is now higher
for CAT2 than CAT4 which means that its
performance is more susceptible to degradation in this
300 TABLE II. COMPARING CAT4 AND CAT2 REGRESSION
Average Number of Generations Needed to Find the
THROUGH DIFFERENT A-VALUES COMPLEXITIES
y = 1.4781x + 49.985
R² = 0.1476 A- R CAT4(R2) CAT2(R2) Sig F
250 Value Change
Static 1.01 0.662 0.438 0.289 0.000
200
Periodic 3.35 0.736 0.541 0.170 0.000
Optimum
150 Chaotic 3.99 0.384 0.147 0.212 0.006
Another way of comparing the two algorithms is to
100
compare the standard deviation for average number of
generation needed to find the solution for given
problem. The three tables below are showing the
50 comparison for the three different complexity values
{A=.101, 3.35, 3.99}. As showing in Table III, CAT4
needed less number of generations to find the solution
0 for the same number of problems. Except for Octagon
0 10 20 30 40 50 topology, CAT4 was more efficient than CAT2.
Landscape Number
TABLE III: COMPARING THE STANDARD DEVIATION FOR
CAT2 VS CAT4, A = 1.0
Fig. 14. CAT4 Regression line over 50 runs for complexity, A = 3.99
A-Value=1.01
300
y = 1.4956x + 50.809 200
Average Number of Generations Needed to Find the
Average number of generation needed to find a solution
R² = 0.2123
180
250
160
140
200 120
100
Optimum
150 80
60
100 40
20
50 0
0
-10 10 30 50 CAT4 Overall STD Dev
Landscape Numbers CAT2 Overall STD Dev
Fig. 15. CAT2 Regression line over 50 runs for complexity, A = 3.99
When the complexity increases to A=3.35, CAT4 was TABLE V: COMPARING THE STANDARD DEVIATION FOR
CAT2 VS CAT4, A = 3.99
outperformed by CAT2.With the exception of the first
two topology (L-Best, and Square), CAT2 was more
efficient. CAT2 needed less number of generations to
solve the same given problems when compare with
A-Value=3.99
200
CAT4.
Average number of generations needed to find a solution
TABLE IV: COMPARING THE STANDARD DEVIATION FOR 180
CAT2 VS CAT4, A = 3.35
160
A-Value=3.35
140
200
Average number of generations needed to find a solution
120
180
100
160
80
140
120 60
100 40
80 20
60 0
40
20
0 CAT2 CAT4
VII. CONCLUSIONS AND FUTURE WORK
CAT4 Overall STD Dev
In society, there are many ways to collect and
CAT2 Overall STD Dev distribute problem solving knowledge. Such
mechanisms include games, auctions, and various
voting mechanisms. Previous work has focused on
Interestingly for Complexity level A=3.99, When the
Independent value auctions. KSs did not have
system is in complete chaotic situation, CAT4 knowledge about the individuals on who they were
outperform CAT2. bidding and did not have consistent bidding strategies.
In this paper, Common Value Auctions were
presented. This framework provided common
knowledge to all KSs about each individual and
supported rule based systems that were used to house
individual KS bidding strategies T
The experimental results suggest that adding the
common value auction to the CAs can enhance the
robustness and the resilience of the algorithm relative
to the commonly used Weighted Majority vote
distribution mechanism. The differences in resilience
were significant across a wide range of dynamic [13] R. G. Reynolds and M. Ali, "The social fabric approach as an
environments tested, from linear to chaotic. The approach to knowledge integration in Cultural Algorithms,"
results effectively demonstrate the impact that in IEEE Congress on Evolutionary Computation, 2009.
knowledge about social networks can have on the [14] X. Che, M. Z. Ali and R. G. Reynolds, "Weaving the Social
Fabric: The Past, Present, and Future of Optimization
sustainability of a Cultural system. Problem Solving with Cultural Algorithms," in AAAI Fall
However, it was clear that as the environment of Symposium: Complex Adaptive Systems, Arlington,
the Cultural Algorithm became increasingly chaotic, Virginia, 2010.
there was a shift from the need to sustain the culture [15] R. G. Reynolds and L. Kinnaird-Heether, "Optimization
through adaptations to that of survival. The presence problem solving with auctions in Cultural Algorithms,"
of additional knowledge in CAT helped in that regard. Memetic Computing, vol. 5, pp. 83-94, 2013.
The question remains as to what type of information [16] R. G. Reynolds and L. Kinnaird-Heether, "Problem solving
about social networks will be particularly useful in using social networks in Cultural Algorithms with auctions,"
in IEEE Congress on Evolutionary Computation, San
guiding complex social systems into even more Sebastian, Spain, 2017.
complex global environments. That is the focus of
[17] Y. A. Gawasmeh and R. Reynolds, "A Computational Basis
future work. for the Presence of Sub-Cultures in Cultural Algoithms," in
IEEE Symposium on Swarm Intelligence, Orlando, FL ,
2014.
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