Effects of Combined Human Decision-Making
Biases on Organizational Performance
Silvia Berlinger and Friederike Wall
Controlling and Strategic Management,
Alpen-Adria-Universitaet Klagenfurt, 9020 Klagenfurt, Austria
Abstract. As extensive experimental research has shown that individuals suffer from diverse biases in decision-making. In our paper we analyze
the effects of decision-making biases of managers in collaborative decision processes on organizational performance. The analysis employs an
agent-based simulation model which is based on the NK model. In the
simulations, managerial decisions which are based on different levels of
organizational complexity and different incentive systems suffer from biases known from descriptive decision theory. The results illustrate how
biases in combination with each other and in different organizational
contexts affect organizational performance. We find that, contrary to
intuition, some combinations of biases significantly improve organizational performance while these biases negatively affect organizational
performance when they occur separately. This might evoke considerations whether decision-making should be as rational as possible.
Keywords: Agent-Based Simulation, Decision-Making, Organization
1
INTRODUCTION
According to Simon humans suffer from bounded rationality [1] and, in particular, experience severe difficulties solving complex decision problems.
We know from descriptive decision theory that human decision-making behavior is influenced by several biases such as the recency effect [2],the status quo
bias [3], and the anchor effect [4]. The recency effect tells us that individuals are
more likely to remember information received at a later time than information
received previously. The status quo bias refers to the tendency to overweight
the current performance state. According to the anchor effect individuals set a
mental anchor on which they strongly rely.
A prominent research method to show the effect of biases is the experiment.
In experimental research, biases are usually analyzed under controlled laboratory
conditions. However, it is a challenge to isolate the various biases from each other
in order to examine the impact of the biases in specific situations. Kahneman
and Tversky [5] explored the effects of framed information by providing different
formulations of decision problems to some test groups. The study indicates that
the formulation is a significant concern for the choices made by the test subjects.
An alternative way to analyze decision-making behavior are agent-based simulations [6]. Using this method it is possible to simulate a broad range of constellations of decision-making processes in organizations as well as human behavior
in various characteristics and under various, though controlled conditions. In
particular, agent-based simulation allows for mapping the organizations which
encapsulate the delegation of decisional competencies and collaborative decisionmaking. Furthermore, this method allows for representing further elements of
organizational design like incentive systems and coordination mechanisms. Both
elements serve to align individual decision-making with the overall objective of
the organization.
However, it is rather unlikely that a decision-maker in an organization suffers
from one distinct bias only; instead we have to assume that several biases occur
simultaneously. This leads us to the compelling research question of this paper:
How do biases in combination with each other on the individual decision-makers
site affect the achievement of the overall organizational objective?
In particular, we analyze the effects of some prominent decision-making biases on the performance of collaborative organizations. To control biases in different combinations and on different levels of complexity of collaborative decisionmaking we use an agent-based simulation based on the NK model [7]. Furthermore, we vary the reward structure: given that decision-makers seek to maximize their individual utility in terms of compensation, decision-makers either
have a “myopic” or a firmwide perspective when making their choices. Thus,
either only the contribution of the individual decision-maker to overall performance is rewarded or the performance, that the entire organization achieves is
rewarded. The remainder of the paper is organized as follows: The subsequent
section presents the agent-based simulation model including the organizational
structure with collaborative decision-making and the decision-making biases under investigation. In Section 3 the main results of our simulations are presented
and discussed. The final section gives some concluding arguments and refers to
further research work.
2
AGENT-BASED SIMULATION-MODEL
The agent-based simulation model is based on the NK model as introduced by
Kauffman [7]. This model has been adopted to the analysis of organizations
by many management scholars [8–10] and used for analyzing decision-making
with imperfect information [11]. However, to the best of our knowledge, the NK
model has not yet been used for analyzing managerial decision-making against
the background of distinct decision-making biases. In our simulation model the
structure of the decision-making organization includes departments, competencies of decision-makers, and incentive systems. With respect to the organizational
structure our model is similar to Siggelkow and Rivkin [10]. Beyond that, our
decision-makers suffer from biases which we regard to be the distinctive feature
of our model.
In prior research human biases have been modeled and implemented in several ways like in [12] where the anchoring effect of consumer price negotiations
within the bargaining-zone model (the bargaining zone is the difference between
the buyer’s and seller’s reservation prices) is analyzed. Kant and Thiriot in [13]
describe a cognitive decision agent model, where the decision makers are modeled in a multi-agent system. In this model the authors use agents with one
specific bias for each agent for a simulation of a small experimental financial
market. Furthermore, An in [14] gives an overview of how to model human decisions in coupled human and natural systems within agent-based models, where
environmental consequences affecting future human decisions and behavior are
analyzed. However, as far as collaborative decision-making is concerned, to the
very best of our knowledge, agent-based modeling has not yet been applied.
In order to represent biases we use findings from descriptive decision theory.
We focus our study on three biases: the recency effect, the status quo bias and
the anchor effect. All three biases have in common that they do not directly
relate to human problems in estimating probabilities of uncertain events; instead these biases rather relate to decision-makers’ preferences and perceptions
of information. Thus, the very core of our simulation model is that collaborative
decision-making processes with different combinations of biased decision-making
managers is investigated on the basis of the NK model.
2.1
Organizational Structure
The organization in our model experiences a ten-dimensional binary decision
problem as in [11]. Thus, the managers make decisions di ∈ {0, 1} with i =
1, . . . , 10. As the simulation is based on Kauffmans NK model [7] the organizational performance corresponds to the fitness of the NK model. Within this
model there exists a fitness landscape in which agents seek to improve their
performance by moving from “fitness valleys” to higher “fitness peaks”. In our
model, N defines the number of decisions which the organizations have to make.
K denotes the level of interactions between the decisions. K can range from
0 to N − 1. In case of K = 0, the N decisions are uncorrelated in the fitness
landscape and the landscape has one single peak. In case of K = N − 1 the interactions among decisions are raised to maximum. Each decision affect the fitness
(performance) of all other decisions. In our simulation N = 10. Each decision i
provides a contribution Ci with 0 ≤ Ci ≤ 1. Ci depends on the single decision i,
but with regard to the complexity of the ten-dimensional decision problem also
on other decisions dji with j = 1 . . . K. For simplicity in our model the level K
of interactions is the same for all decisions i. Hence, the performance contribution Ci is given by Ci = fi (di ; d1i . . . dK
i ).
!The contribution Ci follows a uniform
distribution over the unit interval, i.e., [0, 1] to the overall performance V (d).
The overall organizational performance V (d) results from
V (d) =
N
N
1 "
1 "
Ci =
fi (di ; d1i . . . dK
i ).
N i=1
N i=1
(1)
An organization consists of a main office and two departments. Each department
has primary control over a subset of the ten decisions. In our model department 1
has the control over the first five decisions and department 2 controls the second
five decisions. In our model, decision-makers aim at improving performance in
comparison to the current configuration. Each department head makes decisions
in her/his “own” subset of decisions and chooses the best of three partial configurations available in that period. In particular, each department head discovers
two alternative partial configurations and, hence, has three options to choose
from, (1) the status quo, (2) an alternative with one place of the binary problem
space changed and (3) another alternative configuration where two places of the
binary problem space are altered.
Our model incorporates a rather decentral mode of coordination: Each department head chooses the partial configuration that promises the highest individual utility independent of the other department’s choice. Furthermore,
the main office does not intervene in decision-making. Consequently, the tendimensional configuration that is chosen and implemented in a period is the
result of the two departmental choices. Hence, the role of the main office is limited to registering the departmental and the organizational performance realized
at the end of each period and to compensating the department heads according
to their performance.
As mentioned previously, the department heads seek to maximize compensation according to the incentives given. The incentive structure in our model
corresponds to the model of Siggelkov and Rivkin [10]. Thus each department
head is rewarded according to a linear incentive system that uses either the departmental performance or the overall organizational performance as the value
base for compensation. The incentive mode (departmental versus firmwide) is
controlled by the parameter IN Cr . If IN Cr is set to 0 only the departmental
performance is rewarded while IN Cr = 1 indicates that organizational performance is rewarded. The ranking of configuration d by department r is based on
the value base Br (d) given by
Br (d) = Prown + IN Cr ∗ Prresidual
(2)
The own performance of a department Prown is always taken into account while
the residual performance Prresidual only in case that firmwide incentives are given
(i.e., IN Cr = 1). However, in case of cross-departmental interactions, choices
of one department might affect the contributions of decisions the other department is in charge of and vice versa. Hence, it depends on the interaction structure
whether a department head solely has control over the departmental performance
(the “self” structure in our simulation model) or whether the other department’s
decisions affect the departmental performance (the “full”-interdependent structure), and, consequently, also the manager’s compensation.
2.2
Human Biases
The choice of the department heads described in the previous section is also
affected by decision-making biases. As mentioned previously we selected three
different biases known from descriptive decision theory for our current analysis
as mentioned above: the recency effect, the status quo bias, and the anchor effect.
In our model the heads of the two departments might suffer from all of these
biases. However, the intensity of the biases may vary. Furthermore, our model
allows for providing different parameters to the managers, but for the sake of
simplicity every manager gets the same parameters in the same weights and combinations. Each department head makes one choice in each period. The biases
come into effect when a department head compares the newly discovered configurations and the status quo. For simplification, we assume that the managers
get to know the three configurations at random order. The perception B̃ of the
actual value base B for compensation (see formula 2) resulting from a certain
configuration d is given by
B̃r (d) = Br (d) ∗ (1 − ((xmax − Xn,d ) ∗ α) + q + (γ ∗ a))
(3)
The perceived value base for compensation is influenced by several parameters:
α is used to represent the recency effect. In our model the recency effect is
represented in that the decision-makers perceives an option the less favorable
the earlier he/she gets to know of that option. The incoming order at point of
time n of a configuration d is mapped with Xn,d ∈ {1, 2, 3}. xmax denotes the
maximum order of incoming configurations for rating and is set to xmax = 3,
because there are three options in each decision-making cycle. By multiplying the
actual order of the configuration for rating by the recency effect α with ((xmax −
Xn,d ) ∗ α) we are able to assign the adequate weighting to the configuration
depending on the actual order. This means that an option which is shown to the
manager-agent at last is weighted higher than an option incoming with number
two or one (in this order). For mapping the status quo effect we introduced
the q term. If the incoming performance does not correspond with the status
quo, q is set to zero, thus it is not effective. Otherwise q influences the rating
of the configuration. This means, if the configuration shown to the manageragent is the status quo it is weighted with the bias strength. The anchor effect
is mapped by inserting γ. It is weighted on the value base for compensation
of the very first configuration A, that the managers know within the adaptive
walk: The organization is randomly thrown in the performance landscape; in our
model the randomly assigned initial position serves as an anchor for decisionmaking and, hence, influences the subsequent decisions in the search process.
By adding (γ ∗ A) to the base a more or less intensive anchor effect is revealed
corresponding to the level of intensity of γ. In order to investigate the combined
biases we consider a simple additive coherence of them and, hence, we get the
formula (1−((xmax −Xn,d )∗α)+q +(γ ∗a)). Finally, by multiplying the resulting
bias intensity by the actual value for compensation related to the department’s
new configuration Br (d) (corresponding to formula 3) it is possible to evaluate
the combined effects of the biases in decision-making.
3
RESULTS AND INTERPRETATION
In our simulations artificial organizations are observed for 300 periods. The target measure of our simulation is searching for higher levels of organizational
performance. The simulation starts from a randomly assigned initial position in
the performance landscape. For each setting of parameters 1000 landscapes are
generated with 5 search cycles on each. As mentioned above, our organizations
either have a “self”-contained structure or show “full” cross-departmental interactions. Furthermore, decision-makers are rewarded either on the basis of their
departmental or on the basis of the firmwide performance (i.e., IN Cr = 0 or
IN Cr = 1, respectively). The decision-making biases are simulated at various
levels of intensity. For simplification we simulate bias-strenghts between 0% and
20% in order to analyze different levels of distortion. Thus, bias-intensity ranges
from 0 to 0.2 in steps of 0.05. For examining the impact of the decision-making
biases in combination with each other, we compare always two of them. Table 1 reports final performances for the combinations simulated under different
interaction structures and incentives given.
We start our analysis with a self-contained organization by combining the
recency effect with the status quo bias.
Recency combined with status quo
1
0.95
0.9
0.85
0.8
0.2
0.2
0.15
0.15
0.1
0.1
1
0.95
0.9
0.85
0.8
0.2
0.2
0.15
0.15
0 0
0.1
0.1
0.05
0.05
0.05
0.05
recency
final performance
final performance
Recency combined with status quo
status quo
Fig. 1: recency & status quo, selfcontained, departmental inc.
recency
0 0
status quo
Fig. 2: recency & status quo, selfcontained, firmwide inc.
In case that neither the recency effect nor the status quo bias are effective,
i.e., α = 0 and q = 0 (see Table 1), organizations achieve a high level of performance if departmental incentives are given. By intensifying the status quo
bias the final performance decreases. However, the effects of the status quo bias
on the final performance are apparently compensated by the recency effect (see
Figure 1). Even for high levels of the status quo bias, the final performance increases continuously to a level achieved without any biases if the recency effect
is more intense. In some combinations of bias intensities the final performance
is even higher than without any biases, e.g., α = 0.05 and q = 0.1, performance
is highest. Almost the same performance level was achieved by setting α = 0.1
and q = 0.15, which yields nearly the same performance as if no biases occur. If
the recency effect is effective only, the decrease in final performance is marginal.
Figure 2 shows similar results for organizations that link rewards to firm performance (i.e., IN Cr = 1). The status quo bias causes a decrease in performance,
whereas the intensity of the recency effect apparently enhances performance. In
particular, performance is highest when α = 0.05. But both effects in combination with each other lead to a much better performance at q = 0.2 with α = 0.1
(see Table 1).
Recency combined with status quo
Recency combined with status quo
0.9
final performance
final performance
0.9
0.88
0.86
0.84
0.82
0.2
0.88
0.86
0.84
0.82
0.2
0.2
0.15
0.15
0.1
0.1
0.05
recency
0
0.2
0.15
status quo
Fig. 3: recency & status quo, fullinterdep., departmental inc.
0.1
0.05
0.05
0
0.15
0.1
recency
0 0
0.05
status quo
Fig. 4: recency & status quo, fullinterdep., firmwide inc.
Using the same parameters as above but in the full-interdependent structure
we revealed the following results: As shown in Figures 3 and 4 performance
decreases when status quo bias is effective only. In contrast, the recency effect
increases performance as long as it’s intensity is lower than α = 0.15. Beyond
that level of intensity final performance decreases. A higher level of intensity
can be achieved by combining recency effect with status quo bias (see Figure 3).
In this parameter constellation the departments are rewarded only on the basis
of departmental performance. Performance is highest at α = 0.15 with q = 0
and at α = 0.15 with q = 0.1 (see Table 1). Even for α = 0.2 with q = 0.2
the simulation shows higher performance levels than without any biases. Thus,
biases may enfold beneficial effects. From the analyzes of these four constellations
we can conclude that performance depends on the way in which the status quo
bias and the recency effect are combined. Results indicate, that if managers are
strongly influenced by the status quo bias, the adaptive walks do not lead to high
performance levels. A high influence of the status quo bias in isolated conditions
was found in [15], too. However, if managers underlie a strong status quo bias in
combination with a strong recency effect, it is apparently possible to compensate
the status quo bias or even achieve a higher performance level than without any
biases.
In our study we also analyzed combined effects of the anchor effect and
the status quo bias. As shown in Table 1 the anchor effect does not enhance
Table 1: Effects of biases in organizations with decentral coordination mode
departmental incentives
firmwide incentives
self-contained
recency
0
0.05
0.1
0.15
0.2
status quo
0
0.05
0.97001 0.95763
0.96911 0.97025
0.96950 0.97019
0.96876 0.96989
0.96975 0.96915
0.1
0.94221
0.97184
0.97031
0.96895
0.96941
0.15
0.2
0
0.05
0.92511 0.90749 0.96968 0.94475
0.96000 0.94474 0.97134 0.96929
0.97100 0.96932 0.97044 0.96802
0.96904 0.97059 0.96838 0.96901
0.96969 0.96835 0.96878 0.96823
full-interdependent
0.1
0.91366
0.97081
0.96955
0.96789
0.96847
0.15
0.87884
0.94595
0.97075
0.96819
0.96972
0.2
0.84901
0.91525
0.97135
0.97058
0.96722
recency
0
0.05
0.1
0.15
0.2
status quo
0
0.05
0.87904 0.87420
0.89223 0.88823
0.89314 0.89250
0.89673 0.89401
0.89423 0.89389
0.1
0.86641
0.88042
0.88938
0.89520
0.89478
0.15
0.85680
0.87343
0.88560
0.89353
0.89469
0.05
0.87883
0.89705
0.89572
0.89629
0.89726
0.1
0.86383
0.89788
0.89552
0.89528
0.89336
0.15
0.84821
0.88312
0.89674
0.89517
0.89511
0.2
0.83166
0.86633
0.89693
0.89506
0.89413
anchor
0
0.05
0.1
0.15
0.2
status quo
0
0.05
0.96838 0.95813
0.96708 0.95696
0.96774 0.95812
0.96925 0.95769
0.96985 0.95817
0.1
0.94342
0.94157
0.94268
0.94240
0.94145
0.15
0.2
0
0.05
0.92517 0.90903 0.96968 0.94475
0.92712 0.90810 0.96891 0.94445
0.92579 0.90809 0.96994 0.94522
0.92535 0.90747 0.96998 0.94536
0.92728 0.90937 0.96741 0.94409
full-interdependent
0.1
0.91366
0.91279
0.91212
0.91243
0.91262
0.15
0.87884
0.88182
0.88075
0.88096
0.87959
0.2
0.84901
0.85124
0.84883
0.84852
0.84809
anchor
0
0.05
0.1
0.15
0.2
status quo
0
0.05
0.88188 0.87541
0.88281 0.87529
0.88135 0.87355
0.88088 0.87570
0.88090 0.87598
0.1
0.86650
0.86669
0.86540
0.86783
0.86684
0.15
0.85720
0.85567
0.85793
0.85795
0.85760
0.05
0.87883
0.87971
0.88013
0.88115
0.88090
0.1
0.86383
0.86477
0.86447
0.86370
0.86472
0.15
0.84821
0.84716
0.84637
0.84658
0.84613
0.2
0.83166
0.83164
0.83273
0.83004
0.83173
anchor
0
0.05
0.1
0.15
0.2
recency
0
0.96838
0.96708
0.96774
0.96925
0.96985
0.05
0.97059
0.96910
0.96971
0.96960
0.96971
0.1
0.97084
0.97114
0.96886
0.96969
0.96983
0.15
0.2
0
0.05
0.96910 0.96839 0.96968 0.97134
0.96776 0.96988 0.96891 0.96965
0.97066 0.96912 0.96994 0.96971
0.96947 0.96973 0.96998 0.96939
0.97105 0.97069 0.96741 0.97001
full-interdependent
0.1
0.97044
0.96967
0.97100
0.97019
0.96850
0.15
0.96838
0.96875
0.96897
0.96889
0.96925
0.2
0.96878
0.96881
0.96767
0.96732
0.96943
anchor
0
0.05
0.1
0.15
0.2
recency
0
0.88188
0.88281
0.88135
0.88088
0.88090
0.05
0.89027
0.89162
0.88947
0.89091
0.89051
0.1
0.89473
0.89296
0.89370
0.89225
0.89462
0.15
0.89383
0.89600
0.89500
0.89739
0.89430
0.1
0.89298
0.89712
0.89450
0.89591
0.89560
0.15
0.89640
0.89564
0.89750
0.89465
0.89639
0.2
0.89410
0.89489
0.89481
0.89594
0.89372
0.2
0
0.84794 0.89469
0.86759 0.89685
0.87935 0.89298
0.88985 0.89640
0.89256 0.89410
self-contained
0.2
0
0.85121 0.89469
0.84982 0.89551
0.84839 0.89570
0.84801 0.89441
0.84673 0.89422
self-contained
0.2
0.89375
0.89425
0.89368
0.89684
0.89504
0
0.89469
0.89551
0.89570
0.89441
0.89422
0.05
0.89685
0.89394
0.89649
0.89595
0.89673
The confidence intervals vary between 0.006 and 0.007 for a confidence level of 0.001.
or mitigate the status quo bias, neither in the self-contained nor in the fullinterdependent structure: For a given level of status quo bias our simulations
yield similar levels of performance for all levels of the anchoring effect. However,
our study reveals a clear trend to lower performance. This is caused by decisionmakers that underlie an intense status quo bias. Such a trend was analyzed
by Samuelson and Zeckhauser in [3] for diverse economic phenomena like the
difficulty of changing public policies, preferred types of marketing techniques,
and for the nature of competition in markets.
Anchor combined with recency
Anchor combined with recency
0.975
final performance
final performance
0.975
0.97
0.965
0.96
0.2
0.2
0.15
0.15
0.1
0.1
0.05
anchor
0.97
0.965
0.96
0.2
0.2
0.15
0.15
anchor
recency
Fig. 5: anchor & recency,
contained, departmental inc.
0.05
0.05
0.05
0 0
0.1
0.1
self-
0 0
recency
Fig. 6: anchor & recency,
contained, firmwide inc.
self-
Finally, we investigated combined effects of recency and anchor effect for the
self-contained and the full-interdependent structure. Figures 5 and 6 show the
results for departmental and firmwide incentives for the self-contained structure.
Regarding the departmental incentive mode, the plot is rather scattered. Table
1 shows that the highest performances are achieved at α = 0.1 with γ = 0.05,
α = 0.15 combined with γ = 0.2 followed by α = 0.1 with γ = 0.05, and α = 0.15
combined with γ = 0.1. Thus, the combination of recency and anchor effect
results in noticeably high performance levels in the decision-making process of
managers when there is a strong influence of recency effect combined with low
intensity of anchor effect. However, also when the intensity of both effects is high
performance results are very good. The performance achieved without any bias
is lower. Hence, an organ ization with managers that suffer rather intensely from
the recency and the anchor effect makes better decisions than an organization
with rational decision-makers.
The results for firmwide incentives are shown in Figure 6. In this parameter
constellation the best result is achieved at α = 0.05 with γ = 0 followed by
α = 0.1 with γ = 0.1, and α = 0.1 with γ = 0. Thus, the recency effect α has
even a higher influence in a full-interdependent structure than in a self-contained
structure. The two best performances show a moderate impact of the recency
effect and low impact of the anchor effect. Finally, we revealed good performance
at a medium α combined with a low γ.
Anchor combined with recency
Anchor combined with recency
0.9
final performance
final performance
0.9
0.895
0.89
0.885
0.88
0.2
0.2
0.15
0.15
0.1
0.1
0.05
anchor
0
0.895
0.89
0.885
0.88
0.2
0.2
0.15
0
0.15
0.1
0.05
0.1
0.05
recency
Fig. 7: anchor & recency,
interdep., departmental inc.
full-
anchor
0.05
0 0
recency
Fig. 8: anchor & recency,
interdep., firmwide inc.
full-
Finally, we analyzed the recency effect in combination with the anchor effect in a full-interdependent structure. Figure 7 shows the performance levels
achieved with departmental incentives. In comparison to the results of the same
constellation in a self-contained structure (see Figure 5) the anchor effect seems
to affect performance only slightly. The performance levels achieved are quite
similar for all levels of the anchor effect at a given level of the recency effect. Table 1 shows highest performance at α = 0.15 with γ = 0.15 followed by α = 0.2
with γ = 0.15. However, the benefit of the recency effect is found again.
Similar to Figure 6, Figure 8 shows a quite scattered picture too. High performance levels are detected at α = 0.15 with γ = 0.1 followed by α = 0.1 with
γ = 0.05. Although no specific trend is revealed in this case, but some peaks
are identified at high α values. Results are similar in the self-contained and the
full-interdependent structure.
4
CONCLUSION
The results suggest that, contrary to intuition, human decision-making biases do
not necessarily reduce the overall performance achieved in collaborative decisionmaking processes. Instead, we found that in some constellations decision-making
biases enfold beneficial effects. Of course, this puts claims for decision-making as
rational as possible into perspective. Furthermore, our simulations reveal that
decision-making biases partially compensate each other. The recency effect appears to compensate the status quo bias. With regard to the adaptive search
processes this result might be explained as follows. The status quo bias on its
own obviously even aggravates the well-known problem of adaptive search processes to stick to the local maximum in the performance landscape and, by that,
to lead to stagnancy. In contrary, the recency effect has the potential to generate
diversity in the search process in that eventually the status quo might be undervalued compared to a newly found alternative. These considerations indicate
that the “right” combination of decision-making biases is of crucial relevance.
However, we also found that organizational characteristics like the incentive
system or the interaction structure among decisions might affect the effects of
the decision-making biases in an organization. Hence, the findings show that it
might be a promising approach to analyze more into detail how the sequence of
information provided to managers affects decision-making. Moreover a combination with experimental research to the simulation method might be a further
approach. Furthermore, our model encapsulates three selected decision-making
biases. Beyond these, for example, framing effects could be integrated in order
to analyze the effect of the representation of information on decision-making.
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