=Paper=
{{Paper
|id=Vol-1578/paper11
|storemode=property
|title=How to Manage the Information Sources' Trustworthiness in a Scenario of Hydrogeological Risks
|pdfUrl=https://ceur-ws.org/Vol-1578/paper11.pdf
|volume=Vol-1578
|authors=Alessandro Sapienza,Rino Falcone
|dblpUrl=https://dblp.org/rec/conf/atal/SapienzaF16
}}
==How to Manage the Information Sources' Trustworthiness in a Scenario of Hydrogeological Risks==
https://ceur-ws.org/Vol-1578/paper11.pdf
How to manage the information sources’ trustworthiness
in a scenario of hydrogeological risks
Alessandro Sapienza Rino Falcone
ISTC - CNR ISTC - CNR
Rome, Italy Rome, Italy
alessandro.sapienza@istc.cnr.it rino.falcone@istc.cnr.it
Abstract
In this work we present a study about cognitive agents that have to
learn how their different information sources can be more or less trust-
worthy in different situations and with respect to different hydrogeolog-
ical phenomena. We introduced an ad-hoc Bayesian trust model that
we created and used in the simulations. We also describe the realized
platform that can be manipulated in order to shape many possible sce-
narios. The simulations are populated by a number of agents that have
three information sources about forecasts of different hydrogeological
phenomena. These sources are: a) their own evaluation/forecast about
the hydrogeological event; b) the information about the event commu-
nicated by an authority; c) the behavior of other agents as evidence
for evaluating the dangerous level of the coming hydrogeological event.
These weather forecasts are essential for the agents in order to deal
with different and more or less dangerous meteorological events requir-
ing adequate behaviors. We consider in particular in this paper some
specific situations in which the authority can be more or less trust-
worthy and more or less able to deliver its own forecasts to the agents.
The simulations will show how, on the basis of a training phase in these
different situations, the agents will be able to make a rational use of
their different information sources.
1 Introduction
One of the main problems we have for understanding and foreseeing any domain and world, is not just to access
to the different information sources about that domain, but also to be able to evaluate the trustworthiness of
those sources.
In particular, the same source not necessarily has the same degree of trustworthiness in any situation and
context, but could change its own reliability on the basis of different external or internal factors. For example,
in the domain of the weather forecasts, we know that the mathematical models used for this task are quite
reliable when referring to temporally close events (next 6-24 hours), while are quite approximate if referring to
Copyright c by the paper’s authors. Copying permitted only for private and academic purposes.
In: J. Zhang, R. Cohen, and M. Sensoy (eds.): Proceedings of the 18th International Workshop on Trust in Agent Societies,
Singapore, 09-MAY-2016, published at http://ceur-ws.org
1
long-term events (1-4 weeks). And we also know that these forecasts can change their reliability on the basis of
the kind of phenomenon they are evaluating.
So can be very relevant to have different information sources and also to know how trustworthy they are in
different contexts and situations.
On the other hand, trying to put together information coming from different information sources can be an
uneasy task. It is necessary to have strategies to do it, especially in presence of critical situation, when there
are temporal limits to get decision and a wrong choice can lead to an economical loss or even to risk life.
As said, the necessity of integrating sources on different scopes can be very useful in order to make a
well-informed decision. In case of the weather forecast we can consider different sources: official bulletin of
authorities, the observation of other agents behavior and of their decisions during the meteorological event, the
direct evaluation and competence of the same agents as the basis for their own decisions.
Some of these sources are not correlated among them: a forecast is referred to mathematical model of the
weather linked to its previous data, while a direct evaluation can be based on a current human perception of
the phenomenon (with its potential psychological and perceptive bias). Then, integrating these sources becomes
essential and at the same time it is necessary to identify and take into account their trustworthiness.
In our view [Castelfranchi and Falcone, 2010] trusting an information source (S) means to use a cognitive
model based on the dimensions of competence and reliability/motivation of the source. These competence and
reliability evaluations can derive from different reasons, basically:
• Our previous direct experience with S on that specific kind of information content.
• Recommendations (other individuals Z reporting their direct experience and evaluation about S) or Reputa-
tion (the shared general opinion of others about S) on that specific information content [Conte and Paolucci,
2002; Jiang et al, 2013; Sabater-Mir, 2003; Sabater-Mir and Sierra, 2001; Yolum and Singh, 2003].
• Categorization of S (it is assumed that a source can be categorized and that it is known this category),
exploiting inference and reasoning (analogy, inheritance, etc.): on this basis it is possible to establish the
competence/reliability of S on that specific information content [Burnett et al, 2010; Burnett et al, 2013;
Falcone and Castelfranchi, 2008; Falcone et al, 2013].
However in this paper, for sake of simplicity, we use just the first kind of the three reasons above described: the
direct experience with each source.
Our agents do manipulate their values of trust (we consider the feedback effects and the trust dynamics). In
practice each agent evaluates if an information source was corrected with its own prediction and, on the basis
of this evaluation, decides to increase its trustworthiness or decrease it.
We start from agents equally trusting the three different sources (neutral agents) and then apply to them
a training period experimenting different weather scenarios. We do this with the presence of four kinds of
authorities: reliable and strongly communicative, reliable and weakly communicative, not reliable and strongly
communicative, and not reliable and weakly communicative.
Our investigation is about: are the agents able to learn the more trustworthy sources? Are they able to
intelligently integrate these sources? Are the agents performances coherent with the trustworthiness of the
sources they are following? Are we able to extract useful information from these simulations for situations of real
cases? In the paper we show how, with a certain limit of approximation, we can give some useful and interesting
indications.
2 The trust model
Given the complexity of simulations, we chose to use a relatively simple trust model, unifying many parameters
in just one.
2
Trust decision in presence of uncertainty can be handle using uncertainty theory [Liu, 2014] or probability
theory. We decided to use the second approach, as in this platform agents know a priori all the possible events
that can happen and they are able to estimate how much it is plausible that they occur. In particular we exploit
Bayesian theory, one of the most used approach in trust evaluation [Melaye and Demazeau, 2005; Quercia et al,
2006; Wang and Vassileva; 2003].
In this model each information source S is represented by a trust degree called TrustOnSource, with 0
≤ T rustOnSource ≤ 1, plus a bayesian probability distribution PDF1 (Probability Distribution Function) that
represents the information reported by S.
The trust model takes into account the possibility of many events: it just split the domain in the corresponding
number of interval. In this work we use three different events (described below), then the PDF will be divided
into three parts.
The TrustOnSource parameter is used to smooth the information referred by S. This is the formula used for
transforming the reported PDF:
N ewV alue = 1 + (V alue − 1) ∗ T rustOnSource
The output of this step is called Smoothed PDF (SPDF). We will have that:
• The greater TrustOnSource is, the more similar the SPDF will be to the PDF; in particular if
T rustOnSource = 1 => SP DF = P DF ;
• The lesser it is, the more the SPDF will be flatten; in particular if T rustOnSource = 0 => SP DF is an
uniform distribution with value 1.
The idea is that we trust on what S says proportionally to how much we trust S. In words, the more we trust
S, the more we tend to take into consideration what it says; the less we trust S, the more we tend to ignore its
informative contribution.
We define GPDF (Global PDF) the evidence that an agent owns concerning a belief P. Once estimated the
SPDFs for each information source, there will be a process of aggregation between the GPDF and the SPDFs.
Each source actually represents a new evidence E about a belief P. Then to the purpose of the aggregation
process it is possible to use the classical Bayesian logic, recursively on each source:
f (P E) = (f (EP ) ∗ f (P ))/f (E)
where: f (P |E) = GP DF (the new one)
f (E|P ) = SP DF ;
f (P ) = GP DF (the old one)
In this case f(E) is a normalization factor, given by the formula:
R
f (E) = f (EP ) ∗ f (P )dP
In words the new GPDF, that is the global evidence that an agent has about P, is computed as the product
of the old GPDF and the SPDF, that is the new contribute reported by S. As we need to ensure that GPDF is
still a probability distribution function, it is necessary to scale it down2 . This is ensured by the normalization
factor f(E).
1 It is modeled as a distribution continuous in each interval
2 To be a PDF, it is necessary that the area subtended by it is equal to 1.
3
2.1 Feedback on trust
We want to let agents adapt to the context in which they move. This means that, starting from a neutral trust
level (that does not imply trust or distrust) agents will try to understand how much to rely on each single
information source.
To do that, they need a way to perform feedback on trust. We propose to use weighted mean. Given the two
parameters α and β 3 , the new trust value is computed as:
newT rustDegree = α ∗ oldT rustDegree + β ∗ perf ormanceEvaluation
α+β =1
Where oldTrustDegree is the previous trust degree and the performanceEvaluation is the objective evaluation of
the source performance. This last value is obtained comparing what the source said with what actually happened.
Considering the PDF reported by the source, and remembering that it is split into three parts, we will have that:
1. The estimated probability of the event that actually occurred is completely taken into account;
2. The estimated probability of the event immediately near to the actual one is taken into account for just 1/3.
We in fact suppose that even if the evaluation in not right, it is not even completely wrong.
3. The rest of the PDF is not considered.
Lets suppose that there has been a critical event. A first source reported a 100% probability of critical event;
a second one a 50% probability of critical event and a 50% of medium event; finally a third one asserts 100% of
light event
Figure 1: (a) A source reporting a 100% probability of critical event. (b) A source reporting a 50% probability
of critical event and 50% probability of medium event. (c) A source reporting a 100% probability of light event
The performance evaluation for them will be:
1. Source1: 100%
2. Source2: 50% + (50/3)% = 66.67%
3. Source3: 0%
3 The platform
Exploiting NetLogo [Wilensky, 1999], we created a very flexible platform, where a lot of parameters are taken
into account to model a variety of situations.
3 Of course changing the values of α and β will have an impact on the trust evaluations. With high values of α/β, agents will
need more time to get a precise evaluation, but a low value (below 1) will lead to an unstable evaluation, as it would depend too
much on the last performance. We do not investigate these two parameters in this work, using respectively the values 0.9 and 0.1.
In order to have good evaluations, we let agents make a lot of experience with their information sources.
4
3.1 The context
The basic idea is that, given a population distributed over a wide area, some weather phenomena happen in the
world with a variable level of criticality. These weather phenomena happen in the world in a temporal window
of 16 ticks.
The world is populated by a number of cognitive agents (citizens) that react to these situations, deciding how
to behave, on the basis of the information sources they have and of the trustworthiness they attribute to these
different sources: they can escape, take measures or evaluate absence of danger.
In addition to citizens, there is another agent called authority. Its aim is to inform promptly citizens about
the weather phenomena. The authority will be characterized by an uncertainty, expressed in terms of standard
deviation, and by a communicativeness value, that represents the probability that it will be able to inform each
single citizen.
3.2 Information Sources
To make a decision, each agent can consult a set of information sources, reporting to it some evidence about the
incoming meteorological phenomena. We considered the presence of three kinds of information sources (whether
active or passive) for agents:
1. Their personal judgment, based on the direct observation of the phenomena. Although this is a direct and
always true (at least in that moment) source, it has the drawback that waiting to see what happens could
lead into a situation in which it is no more possible to react in the best way (for example there is no more
time to escape if one realizes too late the worsening weather).
2. Notification from authority: the authority distributes into the world weather forecast with associated dif-
ferent alarm signals, trying to prepare citizens to what is going to happen. It is not sure that the authority
will be able to inform everyone
3. Others behavior : agents are in some way influenced by community logics, tending to partially or totally
emulate their neighbors behavior.
The notification from the authority is provided as a clear signal: all the probability is focused on a single event.
Conversely, the personal judgment can be distributed on two or three events with different probabilities. This
can also be true for others behavior estimation as the probability of each event is directly proportional to the
number of neighbors making each kind of decision. If no decision is available, the PDF is a uniform distribution
with value 1.
3.3 Agents’ Description
At the beginning of the simulation, the world is populated by a number of agents. These agents have the same
neutral trust value 0.5 for all their information sources. This value represents a situation in which agents are
not sure if to trust or not a given source, as a value of 1 represents complete trust and 0 stands for complete
distrust.
Agents are also characterized by a decision deadline, expressed in ticks, that determines the moment in which
agents will make a decision.
The unique difference between them relies on how much they are able to see and to read the phenomena. In fact,
in the real world not all the agents have the same abilities. In order to shape this, we divided equally agents into
three set:
1. Good evaluators: agents that are able to see 15 ticks of event. They will be quit always able to detect
correctly the event, then we expect them to rely mainly on their own opinion.
2. Medium evaluators: agents that are able to see 14 ticks of event. They can detect the event, but not good
as the previous category.
3. Bad evaluators: agents that are able to see 13 ticks of event. Quite often, they will detect two possible
events, but they will need another source to decide between them.
5
3.4 World Desctiption
The world is made by 32x32 patches, which wraps both horizontally and vertically. It is geographically divided
in 4 quadrants of equal dimension, where agents are distributed in a random way.
Figure 2: A world example. There are 200 agents plus the authority, represented by the yellow house in the
center.
The quadrants differ in the possible weather phenomena that happen, modeled through the presence of clouds.
The events are modeled so that agents cant be completely sure of what is going to happens:
1. Critical event: a tremendous event due to a very high level of rain, with possible risks for the agents sake;
it is presented through a 16 ticks sequence of 3 clouds;
2. Medium event: it can cause possible damage to house or streets, but there is not health hazard; it is
composed by a 16 ticks sequence of 2 or 3 clouds (or by a sequence of 13 ticks at the beginning followed by
at least a couple of (2,3) in any sequence). To let this event be similar to the critical one, the 50% of the
times we force the firsts 13 ticks to be equal to 3.
3. Light event: there is not enough rain to make any damage. It is composed by a 13 ticks sequence of 2/3
ticks followed by 2 ticks that can assume one of the value 0,1,2,3 and then a 0. Then, this event can be
confused with a medium one (if not seen in its completeness).
As seen, these phenomena are not instantaneous, but they happen progressively in time, adding a given number
of clouds on each tick until the phenomenon is completed.
Figure 3: An example of a world after an event. Starting from the quadrants in the upper left and proceeding
clockwise, we can see events 3, 1, 3 and 2.
The four quadrants are independent from each other but there can be an indirect influence as agents can have
neighbors in other quadrants. In each quadrant, each event has a fixed probability to happen:
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1. 10% for critical event;
2. 20% for medium event;
3. 70% for light event.
These events are also correlated to the alarms that the authority raises. In fact, as previously said, the author-
ity is characterized by a standard deviation. We use it to produce the alarm generated by the authority and
from it depends the correctness of the prediction. In particular, we considered four kinds of authorities: reli-
able and strongly communicative, reliable and weakly communicative, non reliable and strongly
communicative, non reliable and weakly communicative.
3.4.1 Own Evaluation
How should agents evaluate the phenomena they see? We propose an empirical way to evaluate them, taking
into account how phenomena are generated and how they can evolve.
Considering what we just said, agents can see a sequence of 3 clouds or a sequence of 2 and 3 clouds. The first
one can lead to a critical or a medium event, the second one to a medium or light event.
Table1 provides a complete decryption of how agents evaluate what they see:
Programmatically, agents make a pattern matching of what they see, respecting the order of the table. As
readers can notice, there are just a few cases in which agents are completely sure of what is going to happen.
Table 1: This table provides a comprehensive description of agents evaluation in each possible situation
Event seen Probability of Probability of Probability of
critical event medium event light event
15 ticks of 3 90% 10% 0%
14 ticks of 3 80% 20% 0%
13 ticks of 3 50% 50% 0%
12 ticks of 3 10% 80% 10%
11 ticks of 3 5% 70% 25%
13 ticks of 3 0% 80% 20%
and 1 tick of 2
13 ticks of 3 0% 100% (full-blowen) 0%
followed by (2,2), medium event
(2,3) or (3,2)
15 ticks of 2 or 3 0% 90% 10%
14 ticks of 2 or 3 0% 80% 20%
13 ticks of 2 or 3 0% 50% 50%
12 ticks of 2 or 3 0% 20% 80%
11 ticks of 2 or 3 0% 10% 90%
Any other case 0% 0% 100%
3.5 Workflow
Each simulation is divided into two steps. The first one is called ”training phase” and has the aim of letting
agents make experience with their information sources, so that they can determine how much each source is
reliable.
At the beginning of this phase, we generate a world containing an authority and a given number of agents, with
different abilities in understanding weather phenomena.
At the time t0 the authority gives forecast for a future temporal window (composed by 16 ticks) including an
alarm signal, reporting the level of criticality of the event that is going to happen in each quadrant (critic = 3,
medium = 2, light =1). This information will reach each single agent with a probability given by the authority
communicativeness.
Being just a forecast, it is not sure that it is really going to happen. It will have a probability linked to the
precision of the authority (depending from standard deviation). However, as a forecast, it allows agents to
7
evaluate the situation in advance, before the possible event. Event in fact starts at t1 and, as previously said,
lasts for 16 ticks.
During the decision making phase, agents check their own information sources, aggregating the single contributes
according to the corresponding trust values. They estimate the possibility that each event happens and take the
choice that minimizes the risk. Then, accordingly to their own decision-making deadlines, agents will choose
how to behave.
While agents collect information they are considered as ”thinking”, meaning that they have not decided yet.
When this phase reaches the deadline, agents have to make a decision, that cannot be changed anymore. This
information is then available for the other agents (neighborhood), which can in turn exploit it for their decisions.
At the end of the event, agents evaluate the performance of the source they used and adjust the corresponding
trust values. If they havent been reached by the authority, there will not be a feedback on trust but, as this
source was not available when necessary, there will be a reduction of trust linked to the kind of event that
happened: -0.15 for a critical event, -0.1 for a medium event, -0.05 for a light event.
This phase is repeated for 100 times (then there will be 100 events) so that agents can make enough experience
to judge their sources.
After that, there is the textbf”testing phase”. Here we want to understand how agents perform, once they
know how much reliable their source are. In order to do that, we investigate how they perform in presence of a
fixed map [3 1 3 2]. In this phase, we will compute the accuracy of their decision (1 if correct, 0 if wrong).
3.6 The Decision-making Phase
Once consulted all the three sources of information, agents subjectively estimate the probability that each single
event happens:
1. Pcritical event = probability that there is a critical event;
2. Pmedium event = probability that there is a medium event;
3. Plight event = probability that there is a light event.
They will react according to the event that is considered more likely to happen. There are three possible choices:
1. Escape: agents abandon their homes.
2. Take measures: agents take some measure (quick repairs) to avoid possible damages due to weather event;
3. Ignore the problem: agents continue doing their activities, regardless of possible risks.
We assume that there is a time limit for taking a decision. This deadline is fixed to 15 ticks. Agents have to
decide within this moment.
3.7 Platform’s Input
The first thing that can be customized is the number of agents in the world. Then, one can set the value of
the two parameters α and β, used for the sources trust evaluation.
It is possible to change the authority reliability, modifying its standard deviation, and the authority commu-
nicativeness, that represents the probability that each single citizen will receive the message of the authority.
Concerning the training phase, it is possible to change its duration and determine the probability of the
events that are going to happen on each quadrant, while in the testing phase, that lasts just for 1 event, one can
configure what we call the event map: it is the set of the four events relative to the four quadrants, starting
from the one top left and proceeding clockwise.
4 Simulations
Once realized the platform, we decided to use it to investigate how different authority’s behavior affects citizens
choice and the trust they have in their information sources. We believe in fact that authority’s choices affect not
only citizens individually and directly, but also, by a social effect, citizens not directly reached by the authority.
To do that, we investigated a series of scenarios, populated by equal populations, but in presence of different
8
authorities. Then we analyze how citizens respond to these changes, measuring their trust values and the choice
they make in presence of possible risks.
Simulations results are mediated through 500 cases, in order to delete the variability that a single case can have.
4.1 Simulation’s results
Simulation settings:
1. number of agents: 200;
2. α and β: respectively 0.9 and 0.1;
3. authority reliability: we used the value 0.3 to shape a very reliable authority (its forecast are correct
about the 90% of time) and 0.9 to shape a non reliable authority (its forecast are correct about the 50% of
time);
4. authority communicativeness: 100% for all the three events (strongly communicative), meaning that
each agent will receive authority’s forecast or 30% for all the three events (weakly communicative), meaning
that each agent will receive authority’s forecast only the 30% of the time;
5. training phase duration: 100 events;
6. probability of the events: 10% critical event, 20% medium event, 70% light event;
7. event map: [3 1 3 2].
To help understanding our results, we are going to show them together. Let us start from the trust analysis
Figure 4: Here we represent the average trust value of all the agents on the three information sources in the four
scenarios presented before.
Where RS = reliable strongly communicative, RW = reliable weakly communicative, US = unreliable strongly
communicative, UW = unreliable weakly communicative.
Notice that, as implemented, 1/3 of citizens will quite always be able to quite completely understand the
event, another 1/3 of them can understand quite well what is going to happen but it has less confidence on its
evaluations, the remaining 1/3 of citizens is not a good evaluator. This fact stands for all the scenarios and
that’s why we have a standard value of average self trust.
Let’s start analyzing results case by case. In this first case RS (reliable strongly communicative), the
authority is reliable and all the agents have access to its information. This leads to a high level of authority
trust, but also to a high level of social trust, as the information communicated at the social level is due to the
one coming from the authority and the one directly seen by citizens.
9
Figure 5: Here we represent the average performances of all the agents for the three events in presence of four
different authorities.
In the RW case (reliable weakly communicative) we have a reliable authority, but it rarely communicates its
information. This involves a lack of trust in that source as, even if it is reliable, it is quite always not available.
Socially, this results in a lower lever of social trust, as agents’ decisions are just based on what they see (and
just a part of them is able to directly understand the weather phenomenon). Even the agents’ accuracy lowers
down. Being the authority quite always unavailable, agents have to rely on their own abilities. In fact this effect
is particularly strong for light events, as agents have less probability to read it correctly. Conversely, it is less
strong on critical events, as is it easier to predict.
Let us see what happens when the authority is no more reliable, but it starts communicating wrong
information. In the US case (unreliable strongly communicative), the average authority trust is 62.75%; this
value comes from the fact that the authority reports correct information about the 50% of time. Remember that
it is not true that wrong information is evaluated as 0. If it is not completely wrong (the authority predicted
an event immediately near to the actual one) the evaluation will be 0.33. Most of the agents identify their own
observation as the better information source (as the authority in not so much trustworthy). Then social decision
are mainly influenced by this component, but there is also a minimal influenced of the authority performance.
That is why we have a decrement on social trust.
The last case, the UW (unreliable weakly communicative), is supposed to be the worst one; the authority
is not reliable and is weakly communicative. As we can see, the average authority trust is the lowest among
the four cases, as even when available there is a good probability that the reported information will not be
correct. We have a low value of average social trust, but it is higher than the third case. Again, because of an
unavailable and inaccurate authority, agents will rely on their self. Let’s then try to see the big picture, also
comparing cases to each other.
In the first case (RS) we have the highest values of authority trust: it is a reliable available source, so that
agents can rely on it. The authority trust has a good value also in the US case meaning that, in order to be
trustworthy, it is important to be available to citizens, even if not always with correct information.
Considering the RW and UW cases, they seem to be very similar. Here in fact regardless of authority’s
reliability, trust on the authority is very low. Even the average social trust seems to be the same in the RW and
UW cases. It reaches its maximal point in the RS case, being the other two sources quite always right, and its
minimal point in the US, when the authority reaches all the agents, but it spreads incorrect information.
Summarizing, the US case seems to be good from the authority’s point of view, but it seems to have a
negative social impact.
Taking into account performances, as expected the best case is the RS one; having just trustworthy sources,
agents’ performances are very high. Again the RW and UW cases, in which the authority is unavailable,
are quite the same (actually the UW cases’ values are a little bit lower) meaning that the if the authority is
unavailable, it is no more important how much competent it is. The worst case is the US one. Here we have
10
that all the agents’ performances decreases to their lowest value.
Notice that the event3 is the one who suffers more. To understand this phenomenon it necessary to take into
account the table in section 2.5.1. Let us compare what happen in case of critical event and of light event. In
case of critical event:
1. 1/3 of the population will estimate a 90% probability of critical event;
2. 1/3 of the population will estimate a 80% probability of critical event;
3. 1/3 of the population will estimate a 50% probability of critical event.
Conversely, in case of light event we have that:
1. 1/3 of the population will estimate a 100% probability of light event in the 75% of times, and a 10%
probability of light event in the 25% of times;
2. 1/3 of the population will estimate a 100% probability of light event in the 50% of times, and a 20%
probability of light event in the 50% of times;
3. 1/3 of the population will estimate a 50% probability of light event;
Then, even if it is more difficult for agents to detect a light event, when detected they will have a 100% certainty.
On the contrary it is easier to them to identify a critical event, but not with high level of certainty. This means
that, when there is the influence of another information sources that report wrong information, its influence on
agents will be stronger in case of critical event rather than light event.
Finally, one could ask if it is better to have a reliable authority but not always available (RW) or an unreliable
authority that has a strong presence (US). These results clearly state that the RW case is better, considering
citizens’ performance. This is due to the fact that, even if each individual citizen will receive right information
from the authority about the 27% of the times in the RW case and about the 50% of the times in the US case,
in the RW case the positive effect of the authority is widespread by the social effect. Then even if the authority
is not does not reach everyone directly, it can count on the social effect to do it.
5 Conclusion
In this work we presented an articulated platform for social simulation, particularly suited for studying agents’
choice in presence of critical weather phenomena. To this aim, we realized an ad-hoc Bayesian trust model,
used by agents to evaluate and aggregate information coming from different information sources.
Using this framework, we were able to show some interesting results.
Through the training phase the agents learn to attribute to the different information sources the right values
of trustworthiness and, as a consequence, they are able to perform quite effectively. In particular, two behaviors
of the authorities are interesting: reliable and weakly communicative, not reliable and strongly communicative.
They are a good simulation of the real cases in which the best prediction of a weather event is the more
temporally close to the event itself (when becomes difficult to effectively spread the information: time for
spreading is little). On the contrary, a prediction of a weather event can be effectively spread when there is big
time for the spreading (far from the event), but this is in general a very inaccurate prediction.
Very interesting is the compensatory and integrative role of the social phenomenon (observation of the others’
behavior) that guides the performances of the agents upwards when just one of the two other sources results as
reliable.
6 Acknowledgements
This work is partially supported by the project CLARA-CLoud plAtform and smart underground imaging for
natural Risk Assessment, funded by the Italian Ministry of Education, University and Research (MIUR-PON).
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