=Paper=
{{Paper
|id=Vol-3735/paper_10
|storemode=property
|title=Exploring the Dynamics of Learned, Pre-existing, and
Partial Knowledge in Dependence Networks within
Multi-Agent Systems
|pdfUrl=https://ceur-ws.org/Vol-3735/paper_10.pdf
|volume=Vol-3735
|authors=Alessandro Sapienza,Rino Falcone
|dblpUrl=https://dblp.org/rec/conf/woa/SapienzaF24
}}
==Exploring the Dynamics of Learned, Pre-existing, and
Partial Knowledge in Dependence Networks within
Multi-Agent Systems==
https://ceur-ws.org/Vol-3735/paper_10.pdf
Exploring the Dynamics of Learned, Pre-existing, and
Partial Knowledge in Dependence Networks within
Multi-Agent Systems
Alessandro Sapienza1,* , Rino Falcone1
1
Institute of Cognitive Sciences and Technologies, National Research Council of Italy (ISTC-CNR), 00185 Rome, Italy
Abstract
Dependence networks play a pivotal role in shaping interactions among agents in multi-agent sys-
tems, influencing collaboration, resource management, and overall system performance. In this study,
we explore the effects of limited knowledge on the dynamics of dependence networks within such
systems. Through a series of experiments, we investigate how agents with restricted awareness of
their environment and potential partners navigate within dependence networks and the implications
of their limitations on system outcomes. Our findings reveal that agents with limited knowledge face
significant challenges, including reduced collaboration opportunities, suboptimal resource utilization,
and limited goal achievement. Moreover, we demonstrate the tangible costs associated with acquiring
trustworthiness knowledge and the critical role it plays in optimizing agent interactions. Overall, our
study sheds light on the intricate interplay between knowledge, dependence, and trust in multi-agent
systems, offering insights into strategies for enhancing system efficiency in real-world applications.
Keywords
dependence networks, trust, multi-agent systems, social-simulation
1. Introduction
Social dynamics play a particularly important role in social sciences, since they shed light on
how relationships develop, evolve, and shape human behavior, providing insights crucial for a
deeper understanding of society[1, 2, 3]. This is why the social sciences have devoted so much
interest to the study of these dynamics, in both a theoretical and practical perspective.
Moreover, the dynamics of social bonds play a pivotal role in individual and community
decision-making processes. People are often influenced and conditioned by the beliefs and
actions of their social circles [4]. Understanding how these dynamics affect behavior is vital for
making more accurate predictions and planning effective social and economic interventions.
This is precisely the context in which our research fits. Specifically, we are interested in
investigating dependence networks within hybrid societies comprising both human and artificial
agents, focusing on the social dynamics they give rise to, also in relation to the concept of trust.
Specifically, this work builds upon the results obtained in previous studies[5, 6], to which
we refer for a more comprehensive formalization of the role of dependence. In particular, as
WOA 2024: 25th Workshop "From Objects to Agents", July 8–10, 2024, Forte di Bard (AO), Italy
Corresponding author.
*
$ alessandro.sapienza@istc.cnr.it (A. Sapienza); rino.falcone@istc.cnr.it (R. Falcone)
0000-0001-9360-779X (A. Sapienza); 0000-0002-0300-458X (R. Falcone)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
preliminary works, some limitations were highlighted. Firstly, preliminary studies investigated
dependence relationships within a limited network of agents: in a world where agents are
closely interrelated for the accomplishment of the tasks they need, having few partners available
is certainly a disadvantage. This point was addressed by significantly increasing the number of
available partners. In the scenarios considered here, we introduce a total of 30 agents, notably
more than the 6 in the previous works.
Secondly, there was no effective reinforcement or punitive mechanism for partner selection.
In other words, there was no penalty for agents interacting with untrustworthy partners. In
such a case, adopting a restrictive partner selection strategy became counterproductive because,
without incurring any cost for incorrect interactions, the best strategy was to rely even on
unreliable partners.
This mechanism was addressed by introducing a monetary capital that agents must use to
manage their interactions.
Specifically, within this article, we aim to investigate the role of knowledge in relation to
dependence, addressing the following research questions:
1. How does the necessity to acquire knowledge on others’ trustworthiness impact? Since
trust is closely related to dependence, it is crucial to accurately evaluate partners to decide
whom to rely on. Without this knowledge, there is a "cost" to acquiring it.
2. What happens if agents possess partial and limited knowledge of the world? How does
this aspect affect their performance and that of other agents in the network?
Indeed, the importance of investigating agents operating with restricted knowledge of their
environment and potential partners is evidenced by the great interest in the literature on this
subject[7, 8, 9, 10, 11, 12, 13].
We address the two research questions by introducing a multi-agent simulation and modeling
a network of interconnected agents that need to carry out a series of tasks.
2. Practical Formulation of the Model
In this section, we focus on describing the framework we have developed. We chose to implement
the problem of dependence networks within the block world framework [14, 15, 16]. This domain
was chosen due to its characteristics, which make the block world an interesting study context.
Indeed, despite its conceptual simplicity, the block world offers sufficient complexity to model
a wide range of scenarios and behaviors. Precisely because of this nature, it allows for the
effective exploration of various aspects such as planning, learning, and social cooperation. The
specific world we consider comprises a table and several blocks with different characteristics
(shape, color, weight). Within the simulation context, agents must move specific blocks on the
table to achieve their goals. During the execution of their tasks, they may need to collaborate
with each other. As we will see, from this perspective, different strategies enable agents to
achieve more or less valid performances.
Figure 1 shows how the simulated world appears at the beginning of the simulation.
Figure 1: Example of the simulated world
2.1. The blocks
The blocks are characterized by different shapes (cylinders, cones, cubes, spheres, stars), colors
(red, blue, green, yellow, white), and weights (light, medium-light, medium, medium-heavy,
heavy). Overall, 125 blocks are present in the world. Moreover, blocks can have an owner,
i.e. a single agent who is authorized to change the status of the block in terms of position or
ownership. Initially, all blocks are out of the table. Some of them are assigned to an agent from
the beginning, while others are free and can be claimed by the agents.
Indeed, blocks are a limited resource. They are abundant at the beginning but get consumed
for the agents’ goals. One type of block will be more valuable the rarer it is in the world. We
modeled this phenomenon by introducing a mechanism that takes into account the rarity of
blocks in agents’ transactions: a block will have a minimum value of 1 unit of capital and will
reach a maximum value, set in the simulation, when it remains the only available block of that
type.
2.2. The agents
Agents act in the world in order to realize their goals, i.e. to place specific blocks on the table.
Specifically, each agent is defined in terms of:
• A goal: the type of block the agent needs to move on the table.
• A given competence, which defines how capable an agent is in performing certain tasks.
• A category of membership: we considered two categories: human or artificial agents. The
category influences the characteristics of the agent. Specifically, we assume that humans
can move cylinders and cones, while robots can move cubes and spheres. Both types of
agents are able to move the stars.
• Resources (blocks): initially, each agent possesses a given number of blocks. As the
simulation progresses, this number changes because the agent can yield some blocks or
acquire others.
• Monetary capital: it is the initial capital that citizens have available to manage interactions
with other agents. This capital decreases when the agent delegates a task, while it increases
when they perform tasks for other agents.
• Beliefs: The entire perception and reasoning of the agents are based on beliefs, whether
they are about themselves, the world, or others. Therefore, there is a strong influence of
their personal interpretation of reality. Certainly, these beliefs can vary in accuracy or
even be absent.
• A 𝜎 threshold, which determines how trustworthy its potential partners must be to
consider the dependence with them usable. Such a threshold value, specific to each agent,
has the purpose of verifying that the partner is capable of performing certain actions. Of
course, there remains a certain probability of error.
Agents must collaborate to achieve their goals, considering their subjective dependence
networks, i.e. namely their personal perception of dependencies on others and of others on
themselves. Agents are acquainted with all other agents and blocks in the world.
Introducing categories in this framework allows us, on the one hand, to differentiate the
characteristics of the agents, such as their manipulation and action capabilities in the world.
On the other hand, this enables us to introduce and model processes of inferential reasoning[17,
18, 19, 20].
2.3. Agents’ trust and trustworthiness
In this study, we refer to the concept of trust as modeled in [21]. Trust is considered by the
agents for the selection of dependencies, serving as a mechanism to decide whether to interact
with one partner rather than another.
Within the simulation context, we assume the absence of malicious agents; hence, we choose
not to consider the influence of motivational aspects on the determination of an agent’s trust-
worthiness. For the sake of completeness, it is worth underlining that an agent might have
conflicting motivations regarding a task: for instance, it may not want to give up a block of its
interest as it will be needed for the completion of its sub-goals. However, this does not imply
malicious intent. In such a case, the agent will simply decline the proposed task.
Therefore, we characterize agent trustworthiness in terms of 𝑐𝑜𝑚𝑝𝑒𝑡𝑒𝑛𝑐𝑒, i.e., how effectively
they can accomplish tasks in the world. Competence is defined as a real value within the range
[0,1], where 0 implies a total inability to act, while 1 signifies a guaranteed success.
In the simulated world, we consider three types of tasks:
1. Acquisition of a block.
2. Repositioning of a block.
Since we have no interest in differentiating the values of competence for these tasks, for
computational simplicity, we assume that an agent’s trustworthiness is the same for each of
them. We would like to point out that this is not necessarily true in reality. Indeed, skills on
different tasks usually tend to differ. Nevertheless, considering such a difference would have no
practical impact within our scenario.
An agent is considered capable of achieving a task if it has a probability greater than a given
threshold 𝜎 of accomplishing it. Such a probability is assessed through its trustworthiness evalu-
ation. As mentioned earlier, agents possess a trustworthiness. This is an intrinsic characteristic
of the agent that determines its task execution capability. As such, it cannot be accessed directly,
not even by the agent itself, but it can only be estimated. To estimate the trustworthiness of
agents, we consider a computational model based on the Beta distribution. The Beta distribution
is commonly employed in the analysis of agent trustworthiness [22, 23, 24, 25], especially when
it comes to modeling and estimating success or failure probabilities in complex situations. The
Beta distribution is defined by two parameters, denoted as 𝛼 and 𝛽. As described in Equations 1
and 2, they depend on the estimation of the number of observed successes 𝑛_𝑠𝑢𝑐𝑐𝑒𝑠𝑠𝑒𝑠𝑎𝑥 and
failures 𝑛_𝑓 𝑎𝑖𝑙𝑢𝑟𝑒𝑠𝑎𝑥 of the agent 𝑎𝑥 .
𝛼𝑎𝑥 = 𝑛_𝑠𝑢𝑐𝑐𝑒𝑠𝑠𝑒𝑠𝑎𝑥 + 1 (1)
𝛽𝑎𝑥 = 𝑛_𝑓 𝑎𝑖𝑙𝑢𝑟𝑒𝑠𝑎𝑥 + 1 (2)
In this context, the expected value of the distribution, representing the estimation of the
average trustworthiness 𝑇 𝑟𝑢𝑠𝑡𝑤𝑜𝑟𝑡ℎ𝑖𝑛𝑒𝑠𝑠𝑎𝑥 of an agent 𝑎𝑥 , is given by Equation 3:
𝛼𝑎𝑥
𝑇 𝑟𝑢𝑠𝑡𝑤𝑜𝑟𝑡ℎ𝑖𝑛𝑒𝑠𝑠𝑎𝑥 = (3)
𝛼𝑎𝑥 + 𝛽𝑎𝑥
When an agent 𝑎𝑥 identifies its lack of power to perform a task 𝜏 and the consequent presence
of such power in another agent 𝑎𝑦 , a dependence is explicitly manifested. In this case, if agent
𝑎𝑥 sufficiently trusts agent 𝑎𝑦 , i.e., the assessment that 𝑎𝑥 has made of the trustworthiness of
𝑎𝑦 exceeds the trust threshold 𝜎 of 𝑎𝑥 , then 𝑎𝑥 will contact 𝑎𝑦 for the execution of 𝜏 . In our
framework, in the presence of multiple potential trustees, the choice is made randomly among
those selected as sufficiently trustworthy.
Note that lack of power may either mean a total impossibility to perform the task (for example,
the agent needs to move a certain type of block but does not have the physical characteristics
to do so), or it could also mean that the agent evaluates itself as not sufficiently competent,
based on its internal threshold 𝜎, to execute 𝜏 . In this case, the trustor agent will first attempt to
delegate the task to someone else. In the absence of potential partners, it will still try to execute
the task, as there is no cost associated with doing so.
2.4. Beliefs
Beliefs represent the perceptions and knowledge that agents possess about the state of the
environment, about other agents, and about the relationships between them. These beliefs
influence the decisions and actions of the agents and, consequently, guide the overall evolution
of the simulation. Indeed, their fundamental role becomes even more crucial if beliefs on
dependence networks are also considered.
In our framework, agents possess beliefs about:
• their own goals;
• their own abilities;
• the blocks that exist in the world;
• who the owners of the blocks are;
• the other agents that exist in the world;
• the goals of the other agents;
• the abilities of the other agents;
• the plans of the other agents (they know which ones they possess, not how these plans
are articulated);
• dependencies on actions;
• dependencies on resources (blocks)
• dependencies on plans.
In this contribution, beliefs play a particularly significant role as we investigate what happens
in the presence of limited knowledge. Indeed, in a network of agents, especially in relation to
dependence networks, limited knowledge can significantly impact the beliefs that agents hold.
Remarkably, agents with limited knowledge may fail to recognize all relevant dependencies
within the network. This can result in missed opportunities for collaboration or reliance on
less optimal partners. A similar problem concerns the use of resources. One must believe that a
resource exists in the world and is available in order to utilize it.
3. Simulations
Once the practical model is introduced, we proceed to examine its simulation implementations,
aiming to investigate the effectiveness of using dependence networks. In particular, we also
intend to explore the role of trust in this context. One of the limitations of previous experiments
lies in the small number of agents present in the network, specifically six. Thus, in this case, we
considered a total of 30 agents in the world. The goal is randomly assigned for all agents, thus
avoiding any agent being advantaged or disadvantaged by the complexity of the goal assigned
by the system. In each goal, two out of three components among weight, shape, and color
are specified, leaving the third dimension free. When an agent completes a task, it is assigned
another one, always randomly.
In the experiments, we need a strategy that allows us to identify which agents perform better
and which perform worse. Specifically, we will assign a point each time an agent successfully
completes a goal. Additionally, another metric of fundamental importance is the capital of
the agents. On one hand, this dimension is crucial to account for how well the implemented
delegation strategy optimizes the success/failure ratio of delegated tasks. Furthermore, capital
is a dimension that also models how reliable an agent is considered within the network.
4. Results
We have considered 3 simulation scenarios in which to assess the effectiveness of using de-
pendence networks, specifically comparing their effectiveness with trust. The experiments
were conducted by using agent-based simulation, implementing what was described in the
previous sections on the 3D version of the NetLogo platform [26]. The experiments aim at
investigating the relationship between trust[27] and dependence networks[28, 29]. Specifically,
we are interested in analyzing:
1. The importance of identifying an effective strategy in trusting. This aspect has been
implemented through the trust threshold. We are interested in understanding whether,
in relation to dependencies, it is better to use more restrictive or more open evaluation
strategies.
2. The importance of being trustworthy within the network of agents.
The results we report pertain to a window of 30 interactions among the agents, which is
sufficient to stabilize the interactions. Moreover, what we considered are the results averaged
over 1000 simulations, in such a way as to eliminate the variability introduced by the random
effects on the individual runs.
4.1. First simulation
In this first experiment, we evaluate how the process of acquiring knowledge about partner
trustworthiness impacts on agent performance. To investigate this aspect, we compare two
scenarios: one where agents have to estimate trust from scratch and another where agents,
through a training phase, learn about this dimension beforehand.
Moreover, we will divide agents into two groups of equal size, thus considering the 15 most
trustworthy and the 15 least trustworthy, and compare the performance of these groups.
The experiments were conducted using the following settings:
• duration: 30 time units;
• training phase: 30 time units;
• number of agents: 30;
• number of blocks: 125;
• maximum value of a block: 5;
• percentage of human agents: 50%;
• percentage of artificial agents: 50
• initial capital: 10 unit;
• 2 blocks per agent;
• agents’ 𝑐𝑜𝑚𝑝𝑒𝑡𝑒𝑛𝑐𝑒 randomly assigned in the range [0,1];
• 𝜎 threshold randomly assigned between 0.25, 0.5 and 0.75.
As Table 1 shows, undoubtedly, the most trustworthy agents achieve better results. This
characteristic impacts both the final capital, as these agents receive on average more delegation
requests and have higher incomes, and the average score because, as seen in Section 2.3, agents
who consider themselves less trustworthy tend to delegate more than those who are more
trustworthy.
We then proceed to analyze the trust strategies of the agents, with reference to the partner
selection thresholds: 0.25, 0.5, and 0.75. From Table 2, it emerges that agents with a lower
Table 1
Effect of the training phase on the performance of the most and least trustworthy agents
Training Type Average score Final Capital Income Expenses Total score
no least trustworthy 2.50 7.50 5.58 -8.09 9.99
yes least trustworthy 2.50 6.56 4.19 -7.63 9.06
no most trustworthy 3.48 12.56 10.59 -8.03 16.03
yes most trustworthy 3.35 13.51 10.43 -6.92 16.86
Table 2
Effect of the training phase on the performance as the trust threshold varies
Training Type Average score Final Capital Income Expenses Total score
no 𝜎 = 0.25 3.41 7.63 8.67 -11.04 11.04
yes 𝜎 = 0.25 3.25 7.76 7.69 -9.94 11.01
no 𝜎 = 0.5 3.22 9.20 7.97 -8.77 12.42
yes 𝜎 = 0.5 3.03 9.22 7.05 -7.84 12.25
no 𝜎 = 0.75 2.31 13.17 7.54 -4.37 15.48
yes 𝜎 = 0.75 2.49 13.03 8.09 -4.07 15.51
threshold generally manage to achieve better performance in terms of average score. However,
this happens strongly at the expense of the final capital. Despite having a higher score of 32.3%
compared to agents with a threshold of 0.75, these end up with a lower capital of 72.6%. On
the other hand, using a threshold of 0.75 allows for maintaining a higher capital. This occurs
because in this way agents delegate more cautiously: they manage to delegate less, but when
they do, they have a higher likelihood of success.
Regarding the effect of training, we can observe that, in general, the average scores are
homogenized. Furthermore, examining the ratio of how many tasks are delegated by the most
trustworthy agents compared to the least trustworthy ones, this value stands at 0.82 without
training and 0.69 when training is introduced. Additionally, the ratio of how many tasks are
delegated to the most trustworthy agents compared to the least trustworthy ones stands at 2.20
without training and 3.03 after. Thus, in general, less trustworthy agents delegate more, and
more trustworthy agents receive more delegation requests. The introduction of training further
polarizes these effects.
In practice, the presence of a training phase implies that there is already knowledge available
within the network, and this can be effectively utilized. For example, agents who consider
themselves less trustworthy will directly turn to others for the execution of their tasks. This, in
turn, results in better performance of these agents, which leads to fewer resources available in
the world for other agents.
In general, we observe that by introducing training, expenses are minimized. This is interest-
ing because, in fact, the difference in terms of expenses represents the cost that agents should
pay to learn about the trustworthiness of their potential partners.
4.2. Second simulation
In this second experiment, we are interested in investigating the effects of limited knowledge
on the utilization of dependence networks. To explore the effects of this lack of knowledge, we
introduced agents who have a limited view of the world. These agents are able to observe only
a limited part of the world, which means knowing a restricted number of agents and having
access to a limited amount of resources. In this sense, the knowledge about dependencies that
can be exploited is reduced.
The experiments were conducted using the following settings:
• duration: 30 time units;
• number of agents: 30;
• percentage of agents with limited knowledge about the world: 16.7%, 33.3%, 50%;
• percentage of known world: 25%, 50%, 75%;
• number of blocks: 125;
• maximum value of a block: 5;
• percentage of human agents: 50%;
• percentage of artificial agents: 50
• initial capital: 10 unit;
• 2 blocks per agent;
• agents’ 𝑐𝑜𝑚𝑝𝑒𝑡𝑒𝑛𝑐𝑒 randomly assigned in the range [0,1];
• 𝜎 threshold randomly assigned between 0.25, 0.5 and 0.75.
Within Table 3 and Table 4, the Setting refers to the combination of how many agents with
limited knowledge there are in the world and how limited their knowledge is.
From Table 3, it indeed emerges that knowledge limitations have a significant and strong
impact on the agents’ performance: generally, starting from very low average scores (between
0.85 and 0.89) when agents can observe only 25% of the world, their performance improves
significantly as their knowledge increases, reaching a maximum (between 2.35 and 2.39) when
agents have access to 75% of the world.
Conversely, Table 4 indicates the opposite trend for the remaining agents, who start from
a situation of substantial advantage and gradually achieve fewer goals. This dynamic occurs
because, in the presence of agents with severe knowledge limitations, the remaining agents
can exploit their knowledge more effectively, utilizing more resources than others. By reducing
the knowledge gap between these two categories of agents, the difference in terms of scores
decreases considerably.
Thus, indeed, having limited knowledge severely restricts the effective utilization of depen-
dencies. While this represents a significant disadvantage for these agents, the remaining agents
can exploit this situation to their advantage.
5. Discussion and Conclusions
This work builds upon the findings of previous studies on dependence networks, focusing on
the role of knowledge. In comparison to prior works, this article contributes by investigating the
Table 3
Performance of agents with limited knowledge about the world
Setting Average score Final Capital Income Expenses Total score
16.7% - 25% 0.89 12.46 4.65 -2.19 13.35
16.7% - 50% 1.81 11.11 5.81 -4.71 12.92
16.7% - 75% 2.39 10.39 6.68 -6.29 12.79
33.3% - 25% 0.86 12.25 4.27 -2.02 13.12
33.3% - 50% 1.81 11.01 5.48 -4.47 12.82
33.3% - 75% 2.37 10.46 6.48 -6.02 12.83
50% - 25% 0.85 11.79 3.72 -1.93 12.65
50% - 50% 1.81 10.87 5.10 -4.23 12.68
50% - 75% 2.35 10.33 6.17 -5.84 12.68
Table 4
Performance of agents with complete knowledge about the world, when in presence of agents with
limited knowledge
Setting Average score Final Capital Income Expenses Total score
16.7% - 25% 3.12 9.51 7.16 -7.65 12.63
16.7% - 50% 3.01 9.78 7.41 -7.63 12.79
16.7% - 75% 2.98 9.92 7.71 -7.78 12.90
33.3% - 25% 3.27 8.87 6.22 -7.35 12.14
33.3% - 50% 3.05 9.50 6.84 -7.34 12.55
33.3% - 75% 2.98 9.77 7.31 -7.54 12.75
50% - 25% 3.52 8.21 5.32 -7.12 11.73
50% - 50% 3.14 9.13 6.30 -7.17 12.27
50% - 75% 3,00 9.67 7.02 -7.35 12.68
difference between needing to learn and already knowing information, and between possessing
it entirely or partially, within dependence networks in multi-agent systems. These problems are
also explored in relation to trust, particularly focusing on the trust strategy and the relevance
of being trustworthy.
Remarkably, in both experiments, albeit on different aspects, the value of knowledge clearly
emerges. In its absence, there are significant limitations on the utilization of dependencies, i.e
agents cannot optimally leverage the network for collaborations, which negatively affects their
performance.
In the first experiment, we demonstrate how the lack of knowledge about agents’ trustwor-
thiness results in an actual cost to acquire it. This cost is paid through the direct experience that
agents have with the world and their partners. Initially, agents enter the environment with no
pre-established understanding of who among their peers is trustworthy or not. As a result, they
must engage in interactions and collaborations based on failure and error, which involves both
successful and failed attempts at task delegation. Each failed attempt represents a tangible cost:
it could mean wasted resources, missed opportunities, and time lost in reattempting the same
task or seeking out new partners. Moreover, the process of learning from these interactions
incurs additional cognitive and computational costs as agents need to continually update their
beliefs and strategies based on the outcomes of their engagements. This iterative process of
refining trust assessments underscores a significant investment in terms of effort and efficiency.
The cumulative effect of these costs highlights the critical importance of initial knowledge about
trustworthiness in optimizing agent performance within the network.
Conversely, this situation represents an advantage for the remaining agents, who can effec-
tively exploit resources in the world.
In the second experiment, we analyze what happens to agents with limited knowledge of the
world. These agents face significant interaction limitations, as their restricted understanding of
the environment hampers their ability to identify and connect with potential collaborators. As a
consequence, they often miss out on crucial opportunities for cooperation and resource sharing,
leading to suboptimal performance. Their lack of comprehensive knowledge means they cannot
fully leverage the dependencies within the network, which is vital for achieving complex tasks
that require coordinated efforts. This limited knowledge also results in inefficient use of the
capital they possess, as they are unable to identify the most strategic ways to invest or utilize
their resources. Conversely, this situation represents a distinct advantage for the remaining
agents, who possess a broader and more accurate understanding of the world. These agents can
capitalize on the limitations of their counterparts, effectively exploiting available resources and
maximizing their performance within the network.
Another finding is that more relaxed delegation strategies regarding trust lead to higher
performance, but at a significantly higher cost. It is noteworthy that this scenario considered a
world with strictly finite resources. Indeed, in the absence of such a condition, this strategy
would be further penalized.
This analysis has provided valuable insights into the role of knowledge within dependence
networks. These aspects certainly deserve further investigation. Additionally, it would be
interesting to explore the effects of erroneous beliefs on dependencies. Erroneous beliefs
can significantly influence agents’ behavior, since they can lead agents to take suboptimal or
even harmful actions, as they are based on incorrect information about dependencies among
themselves and other agents. For example, an agent may mistakenly believe to be dependent on
another agent when in fact it is not, or vice versa. This could lead to erroneous decisions in
resource management or collaborative task planning. Therefore, understanding how erroneous
beliefs influence the dynamics of dependence networks is crucial for developing more effective
strategies and improving the overall performance of the multi-agent system. We intend to
investigate this aspect in future work.
Acknowledgments
This work has been partially supported by the project FAIR - Future Artificial Intelligence
Research (MIUR-PNRR).
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