=Paper= {{Paper |id=Vol-3022/paper1 |storemode=property |title=Studying citizens' trust to monitor measures acceptance during COVID-19 pandemic |pdfUrl=https://ceur-ws.org/Vol-3022/paper1.pdf |volume=Vol-3022 |authors=Alessandro Sapienza,Rino Falcone |dblpUrl=https://dblp.org/rec/conf/atal/SapienzaF21 }} ==Studying citizens' trust to monitor measures acceptance during COVID-19 pandemic== https://ceur-ws.org/Vol-3022/paper1.pdf

Studying Citizens’ Trust to Monitor Measures
Acceptance during COVID-19 Pandemic

Alessandro Sapienza and Rino Falcone
Institute of Cognitive Sciences and Technologies,
ISTC-CNR
Rome, Italy, 00185
alessandro.sapienza,rino.falcone@istc.cnr.it

Abstract
Over the last year, the COVID-19 pandemic has strongly affected ev-
eryone’s lives. An unprecedented situation, which required enormous
sacrifices and very stringent limitations. Within this context, trust
has played a crucial role: people decided to trust their institutions to
tackle the pandemic and that trust made the strong restrictive mea-
sures effective. This work aims to study the response of the Italian
population to the early stages of the pandemic. Making use of a survey
addressed to 4260 Italian citizens, we realized an agent-based simula-
tion to model and analyze citizen trust starting from its main cognitive
sub-components, with particular reference to the dimensions of compe-
tence and willingness. The results of this work can be of great interest,
both for understanding what happened in the past, but also for design-
ing effective strategies in the future.
Keywords: COVID-19, Trust, Multi-agent system, Social Simulation

1 Introduction
The first case of COVID-19 was reported in Wuhan, China in December 2019 [Li20] which then spread around
the world. In the last months, global health authorities approved a few vaccines and vaccination has already
started worldwide. Unfortunately, it is not yet clear how long it will take to make these vaccines available to
the vast part of the world population. Since, this problem seems far from being solved[Bon20], there is no other
choice but to prepare to live with the virus for a while longer.
Italy was affected by COVID-19 in late February, being the first European country. After a a few weeks,
the pandemic spread throughout the rest of the country [Tui20]. The only measures available to deal with
the emergency involved highly restrictive social distancing measures [Bak20, Pre20, Jef08], as pandemics spread
exploiting the social and community tendency of individuals [Zng20]. However, establishing costly and drastic
measures would not have been enough, as societal safety was strongly depending on the commitment of individual
citizens[Cai20, Fll20]. In fact, the efficacy of the containment measures relies almost entirely on the citizens’
willingness to cooperate with institutions and such measures would have been vain if a significant percentage of
the population had considered them inadequate or inappropriate, not respecting them.

Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC
BY 4.0).
In: R. Falcone, J. Zhang, and D. Wang (eds.): Proceedings of the 22nd International Workshop on Trust in Agent Societies, London,
UK on May 3-7, 2021, published at http://ceur-ws.org

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Such considerations suggest the need to investigate what happened during the first wave, in order to identify
what led to this high acceptance rate. This analysis could help the authorities realize better intervention policies
and communication strategies in the future [Pag20].
As it is well known, citizens’ trust in public institutions has a key role during pandemics[Sie14], influencing
people’s willingness to adopt recommended behaviour [Lew20]. Indeed, in the first pandemic wave, the Italian
population decided to trust its institutions to face COVID-19[Fal20a] and this has actually led to an exceptionally
positive result.
As [Fel20] stated, understanding how trust is formed and when it breaks down is a key aspect in understanding
large social behavioural trends during this time There is a clear need for a comprehensive theory and a model
of the citizens’ decision-making process. We need a detailed analysis of trust and its cognitive components, to
identify what elements stimulated trust and how trust actually led citizens to compliance. Given the limited
resources, in fact, public authorities need to choose where to focus their efforts. Knowing which cognitive
components should be stimulated to influence at best citizens’ opinion - and, consequently, their behaviour -
would be of great help.
The tool of simulations has been widely used to study this pandemic. It proved to be particularly useful
for reducing the impact of COVID-19 [Cur20], supporting different decisions that arise during a public health
emergency such as this one. Indeed, over the past months, many contributions studied the pandemic using
SIR models [Ana20, Bar20, Fan20] or agent-based simulation [Man20, Cre20, Hun20, Roc20, Sil20] focusing on
the spread of the contagion. If, on the one hand, these issues have widely been addressed, on the other hand,
much less attention has been given to simulating citizens’ acceptance of the restrictive measures. The measures
necessary to reduce the infections and damages of the pandemic have been clearly detected, yet a detailed analysis
of the conditions leading to compliance is still lacking. At this very moment, we believe that such analysis is
fundamental to be better prepared for the future, exploiting the huge amount of data we already gathered in the
past months.
Presenting this study, we mean to contribute to the vast body of knowledge on the COVID-19 pandemic,
using agent-based simulation to model citizens’ decision-making process and, then, analysing the reaction of the
whole community. Specifically, on the basis of a survey addressed to 4260 Italian citizens, we applied linear
regression as an instrument to reproduce citizens’ trust and decision-making model from their main cognitive
sub-components.
In particular, we aim to contribute to the assessment of public policies to control the incidence of COVID-19
in two ways. First of all, we produced a detailed analysis of what happened during the first pandemic wave,
investigating the behaviour of the Italian population. Secondly, based on this detailed analysis, we are able to
make predictions about possible future behaviours, by hypothesizing scenarios of new pandemic waves.

2 Survey and Sample
The survey [Fal20a] was conducted on Italian citizens (N = 4260) between 9 March and 14 March 2020, using a
snowball sampling method to determine the respondents. The main characteristics of the sample are synthesized
in Table 1. It should be noted that the mean educational level of participants is very high: almost three quarters
of respondents have a degree (38%) or post-graduate specialization (34%).
The questionnaire was aimed to investigate the participant’s overall trust towards public authorities, along
with the factors beyond this trust. The questionnaire was based on the socio-cognitive model of trust developed
by [Cas10] and explored participants’ opinions on five main dimensions, in relation to the current COVID-19
crisis:

1. Evaluation of the competence of public institutions;

2. Evaluation of the willingness of public institutions;

3. Purposes and effectiveness of the public institutions’ intervention;

4. Trust and information sources: the most used sources of information and their perceived trustworthiness;

5. Expectations about the future scenarios that will arise, once the COVID-19 crisis is over.

Within this work, we focus on the first three points, considering all the cognitive items related to them. The
questionnaire fully complied with ethical guidelines for human subject research and participation was conditional

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Table 1: Sample characteristics
Regions most affected % (30%) Regions less affected % (70%) Total %
Gender
Men 45 42 43
Women 55 58 57
Total 100 100 100
Age (Mean=46)
18-29 19 11 13
30-39 23 18 19
40-49 23 24 24
50-59 21 28 26
60-69 11 15 14
>70 3 4 4
Total 100 100 100
Educational Level
Middle school 3 2 2
High school 24 27 26
University degree 41 36 38
Post-graduate spe- 32 35 34
cialization
Total 100 100 100
Geographical prove-
nance
Northern Italy 96 7 33
Central Italy 4 53 39
Southern 0 40 28
Italy/islands
Total 100 100 100

on the preliminary approval of an informed consent by each subject; the compilation took an average time of 10
minutes.

3 Trust Model
3.1 Theoretical Formulation
We aim to identify the complex relations between trust and its components, with respect to the context of the
COVID-19 pandemic. The model of trust we realized was inspired by the socio-cognitive model developed by
[Cas10], the same exploited in the original survey [Fal20a]. Looking at the main predictors of trust in order to
perform regression analysis, we chose this approach because it provides a rich and nuanced description of the
various cognitive components of trust. Therefore, we realized an instance of this theoretical model, related to
the specific context of interest.
We consider a population composed by n citizens P = {c1 , . . . , cn } and a public authority P A. Upon the
occurrence of a pandemic C, the citizens need to find someone reliable to carry out a specific task τ , which implies
the identification of the necessary rules and restrictions to face the pandemic threat, ensuring the well-being of
citizens.

τ = (α, g) (1)

In 1, α represents the action. In this specific case, α implies ”to establish the necessary measures”. Actually,
this is not a simple action, but rather a whole class of actions. Concerning g, it is the goal of the task, which in
this case is ”ensuring the well-being of the whole community, thus including ci ”.
Given the nature of the problem and of the domain we are considering, there is only one potential trustee able to
carry out τ , precisely P A. Such role is well defined within the society, and it is established that who is holding it
must also perform τ . Thus, citizens evaluate P A and consider how appropriate it is for τ , within the COVID-19

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pandemic context C. Referring to the socio-cognitive model, trust is formulated as in equation 2.

Trust(ci , P A, τ, C), ∀1 < i < n (2)
The citizen ci will actually decide to trust and to rely on the authority P A to carry out the task τ (which,
in turn, implies the execution of the action α to realize the goal g) just if the trust evaluation on P A is good
enough, i.e. above a certain threshold of acceptance σi .
Putting the theoretical framework into practice, it is necessary to investigate the specific features of trust. In
fact, as it is well known in the literature [Cas00, Ram04], trust is made of different cognitive sub-components.
Among the many, two assume a particular importance: competence and willingness. Competence represents the
set of qualities making the trustee good for the task τ : skills, know how, expertise, knowledge, self-esteem, self-
confidence, and so on. Willingness, the second fundamental dimension, is a prediction of the trustee’s behaviour:
the fact that it is reliable, predictable, that we can count on it. Willingness evaluates weather the trustee is
willing (it really has the intention to do α in order to realize the goal g) and persistent [Cas98, Fal01] (it is not
prone to give up, but it will insist on achieving g). Many other beliefs, depending on different kinds of delegation
and different kinds of agents, enrich and support these two components of trust as an attitude towards the
trustee. Nevertheless, competence and willingness represent the real cognitive kernel of trust. With respect to
the context we are interested in:
1. the trustor ci has to believe that the trustee P A has the ability to realize τ => Belci (CanDoP A (τ ))
2. the trustor ci has to believe that P A is really willing to realize τ => Belci (W illDoP A (τ ))
According to Castelfranchi and Falcone, competence and willingness are the necessary components of trust
and trustworthiness. This does not imply that, in order to believe that P A is trustworthy (and possibly to
decide to trust it), ci should necessarily have a good evaluation of its competence and willingness. Trust, as an
evaluation, is not a yes/no status; ci ’s trust in P A (evaluation of trustworthiness) must be just sufficient enough
to take a risk on it. For instance, it may happens that the competence has a negative evaluation, but the overall
evaluation of the trustee is positive.
Of course, internal change in the trustee (new goals, the need to redirect funds, etc.) or external inputs
(rumors about what is going to happen, dissemination of fake news, etc.) may affect these two components and,
in turn, the evaluation of citizens[Bun20]. We are not interested in modeling the process of knowledge acquisition
and revision of citizens and how this affects trust [Fal20b]. Instead, within this framework, we consider a higher
level representation. Therefore, we focus only on the actual variation of competence and willingness, without
deepening the reasons for this change.
Notice that, in this specific scenario, if ci actually decides to rely on P A for τ , as a consequence, it chooses
to accept the rules and the restrictions that P A established. In other words, in order to successfully pursuit τ ,
in turn, P A will assign a further task τ1 (equation 3) to citizens:

τ1 = (α1 , g1 ) (3)

In equation 3, the action α1 is ”to follow the restrictions that P A established”, while the goal g1 is a sub-
goal necessary for achieving ”the well-being of the whole community” (respect for spacing, cleanliness of hands
and wearing the mask will result in partial acquisition of the overall goal of defending public health). In fact,
only maximizing the number of citizens adopting g1 it will be actually possible to realize the overall goal g, i.e.
ensuring the well-being of the whole community. While the authority P A explicitly aims to protect the whole
community (g as result of the sum of all g1 ), the primary purpose of the citizen ci is, usually, to protect itself
(indeed, it may also understand that it can contribute to achieve g, which is the only way to really save itself).
Maybe ci does not believe g1 or g either. For the sake of completeness, we care to underline that other different
reasons may induce citizens to adopt the goal g1 , even if they do not possess g. As an example, compliance may
aim at achieving other goals, such as avoiding fines. But there may also be more elaborate motivations behind
ci ’s choice, such as compliance to the norms governing the society (the pact at the basis of common coexistence
in an organized society). The citizens ci may not agree with g1 , but it could still decide to adopt g1 (and as a
consequence g) in order to respect social norms.
Besides competence and willingness, many other cognitive variables may come into play during the trust
assessment process. For instance, the subjective importance of the task τ1 , i.e. how important τ1 is for the

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Table 2: Linear regression coefficients for trust on P A
Coefficient
Competence 0.3385
Willingness 0.2558
P A’s goal: to contain the 0.2149
contagion
Importance of own contri- 0.1892
bution
Expectation on others’ be- 0.1169
haviour: full compliance
k -0.8646
trustor. Notice that τ1 does not necessary imply a direct utility, since following the rules could not be directly
useful to the specific citizen, but rather to the whole community. Carrying out τ1 is a necessary precondition to
realize g. Nevertheless, the actual achievement of this goal is related to the execution of τ1 by a significant part
of the population. The intertwined nature of the relationship between g and g1 (respectively τ and τ1 ) introduces
many other cognitive variables, refining the decision-making phase: if the trustor perceives its contribution as
useful for the community and/or for its relatives; if it believes that a significant part of the population will
actually carry out τ1 and then it will be possible to realize g; if it believes that an insufficient number of people
will follow the rules, thus it is not worth accepting these restrictions. Although all these variables will be used to
shape trust, within the simulation we will mainly focus on two specific dimensions: competence and willingness.

3.2 Linear Regression on trust
Linear regression is a linear approach to modelling the relationship between a dependent variable and one or
more explanatory variables. The advantage of such approach is that, unlike in correlation analysis, it does not
simply investigate the relationship existing between two variables, telling us how much one affects the other. On
the contrary, this instrument takes into consideration the entire picture, assessing the interaction of all variables
to determine their overall effect on trust. This tool fits perfectly with the considered problem since, by the means
of appropriate weights, it relates trust to its different cognitive sub-components. Specifically, for each citizens
ci , we are interested in a function modelling its trust evaluation as in equation 4:
q
X
Trust(ci , P A, τ, C) = ki + wh ∗ xh (ci , P A, τ, C) (4)
h=1

While we already have the values of the different variables x1 , x2 , . . . , xq , extrapolating them from the cited
dataset, linear regression provides us with weights w1 , w2 , . . . , wq and constant ki .
We applied linear regression to the whole sample, estimating a unique trust function for all the population.
Data analysis was performed using Weka (version 3.8.3) statistical software [Wit02].

Table 2 reports all the weights used in linear regression. These data confirms a clear and strong link between
trust and the dimensions of competence and willingness. In addition, the difference in weight between competence
and willingness appears clear. Overall, citizens consider competence as more important.
The correlation coefficient is very high (0.7405). Concerning the M AE, it hovers around 0.5 (0.4788). This
value appears more than reasonable, given that trust functions range in [1, 5]. Moreover, while the subjects could
express their evaluation in natural number N, thus excluding the intermediate values, trust functions work in R.
Such values suggest that the defined functions represent a quite well approximation of citizens’ trust assessment.
Nevertheless, our goal is not estimating trust of specific individuals, but that of the whole community. At
community level, M AE is significantly reduced: the estimation of the average community trust is almost exactly
equal to the actual average trust of the sample. In other words, trust estimation at community level is much
more precise (M AE < 0.01) than that on individual subjects.

3.3 Linear Regression on the acceptance threshold
In their theoretical model, Castelfranchi and Falcone state that trust as an evaluation can be defined as a degree.
However, producing an evaluation is only the first step. We are also interested in studying the following cognitive

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phases. We would like to understand when and how this evaluation becomes a decision and then an actual action,
the act itself of trusting and relying on the trustee. As highlighted in [Cas10], trust as a decision or action cannot
be defined as a degree, but it is a boolean variable: true/false, to trust/not to trust.
If the trustor perceives the trust evaluation as good enough (above a certain internal threshold), then it
decides to trust. Otherwise, it excludes this possibility. Referring to the context of our interest, after the
evaluation, the citizen ci compares Trust(ci , P A, τ, C) with an internal threshold σi . Such a comparison will
determine the final decision of ci . As σi is an internal property of the citizen ci , it is not given in advance.
Nevertheless, it is possible to estimate it. First of all, we care underlining that the trust assessment process
we introduced does not simply evaluated an a-priori trust, an abstract evaluation of the trustee P A. On the
contrary, this evaluation takes into account all the specific cognitive dimensions related to the task and the
context (perceived importance of the task, expectation on others’ behaviour, additional reasons for trusting PA,
and so on). Since all these dimensions have already been weighed, with a first acceptable approximation, we
may suppose that σi is around 0.5 (the half of the interval in which the trust value is included). In other words,
if the trust evaluation is greater than half, ci decides to trust P A. Such statement represents a reasonable
choice, both qualitatively and quantitatively, since a trust value over 0.5 actually represents a situation in which
the reasons for trusting are more than the reasons for not doing so.
As a second approach, which we actually implemented in this work, it is possible to estimate the actual value
of σi , on the basis of the answers of the subjects. Practically speaking, we are interested in identifying a trust
threshold such that above this value we may reasonably assume ci effectively trusts P A. In other words, ci
considers useful to follow the rules determined by P A. Then, making again use of linear regression, we linked
trust in P A for COVID-19 management with the evaluation of the measures to face this emergency. Then, we
exploited these functions to determine the acceptance threshold. Basically, we estimated we have estimated
how much trust the citizens need to perceive the measures at least as enough useful (namely, greater than 3 in
Likert scale, or 50%). Remarkably, results from linear regression analysis show that σ values hover around 50%
(51.83%, 3.0733 in Likert scale). This is actually very interesting, as there is an actual correspondence between
the threshold we deduced and the one we computed.

4 Simulations
We developed an agent-based model for a fine-grained computational simulation of the institutional trust of
Italian people during COVID-19 pandemic. Such framework can be used to explain how and to what extent
the individual sub-components have affected trust and, above all, to understand how trust evolves when these
components change. The model has been developed in the environment of NetLogo 5.2 [Wil99].
Within the simulations, we use as input the already cited corpus. Given that we can exploit 4260 subjects,
we considered as many agents in the simulation. We used the items corresponding to the subjects’ answers as
variables to describe and characterize the different agents.
We proceed estimating individuals’ trust on P A, using the framework previously described. Thus,
Trust(P, P A, τ, C) is computed as the mean of individuals’ trust evaluation (equation 5). Given that the popu-
lation P is made of n citizens
Pn
j=1 Trust(cj , P A, τ, C)
Trust(P, P A, τ, C) = (5)
n
The framework we defined can be used to verify the evolution of trust when its different cognitive components
change. In particular, in this study we focused on the two dimensions of competence and willingness, keeping
fixes the values of the other parameters. Given that the subjects were asked to answer in a 5-point Likert scale,
we also considered the variation of competence and willingness with respect to the same scale. Instead, trust
values will be reported both in percentage and in Likert scale.
Of course, within the framework we defined, it would be theoretically possible to consider different dimensions,
which would have a more or less significant impact on trust. Nevertheless, our choice fell precisely on competence
and willingness. The reasons behind this choice are basically two. First of all, Castelfranchi and Falcone
themselves identify these component as the most important for trust, being part of what they defined ”core
trust”. Indeed, the analysis conducted on the subjects confirms their theory, as competence and willingness are
two of the components that most correlate with trust: respectively (R = 0.587, p < 0.0001) and (R = 0.551,
p < 0.0001). In turn, this implies that their variation has also a higher impact on the final trust value.

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Secondly, it has to been considered that P A can directly act on these variables, in order to modify its actual
trustworthiness and citizens’ perception of its competence and willingness. This situation does not hold for the
other variables. Certainly, the authority P A may still decide to work on other dimensions affecting citizens’
trust evaluation (for instance importance of own contribution, expectation on others’ behaviour, etc.). However,
this would be a much more complex way to (possibly) achieve the same result. Even managing to indirectly
manipulate such variables, this would still have a lower impact, as these secondary variables have a weaker
correlation with trust.

5 Results
Below, we start analysing citizens’ reaction in the first pandemic phase. After that, we take into consideration
a series of possible hypothetical scenarios, in which the values of competence and willingness change.

5.1 The first pandemic episode
In the first instance, the realized platform can be used to analyse citizens’ reaction during the first pandemic
episode. As far as it concerns the average trust of the whole population on P A, we estimated Trust(P, P A, τ, C)
= 72.35%. This is definitely a high value, especially if we think of the historical mistrust in the public institutions
in Italy. Such a result confirms the actual decision of the population to rely on public institutions, in order to
face COVID-19.
Again, we stress that the positive outcome of the measures determined by P A is conditional on the acceptance
of these restrictions by a significant percentage of the population. Thus, a generic attempt to raise the average
trust, besides being very challenging, may not be the best strategy to increase citizens’ adoption. As we will see
later, it is undoubtedly true that increasing average trust results in a higher acceptance, but different acceptance
rates may correspond to the same trust value. Identifying the most critical dimensions and working on them
may be a better strategy to optimize the global result, minimizing the effort to increase trust values.

5.2 Analysis of community Trust
In the previous section we provided a picture of what happened in the past months, during the first pandemic
wave. This kind of analysis provides useful hints to understand what worked and what were the weaknesses. In
this section, instead, we consider some hypothetical scenarios, evaluating what might have happened (or what
may happen in future) upon the occurrence of specific conditions. In particular, we verify trust evolution in 81
different simulation scenarios, on the basis of the following experimental setting:

1. number of agents: 4260

2. competence: 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5;

3. willingness: 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5;

Figure 1 shows community trust evolution when the components of competence and willingness change.
Specifically, it varies from a minimum of 22.78% to a maximum of 82.14%. It is worth noting that the curve has
a substantial asymmetry. In the presence of a maximal competence and a minimal willingness, trust is estimated
at 56.56%. On the contrary, with a minimal competence and a maximal willingness, we get 48.29%. Such an
effect is the consequence of a different influence these two cognitive components have on trust. Indeed, subjects
assign a higher weight to competence.
In figure 1, the dark gray area of the curve highlights a situation of substantial average distrust. It describes a
critical situation, which P A needs to avoid at any cost. In fact, in such cases the average trust of the population
is below 40%. In turn, this may result in a strong negative impact on the citizens’ acceptance of the measures.
In contrast, the lighter gray area describes a positive situation, in which the average trust is greater than 60%.
While such general consideration already provides some useful indications, the observation of the whole com-
munity may not be enough. An average value does not describe how trust is actually distributed among the
population.

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Figure 1: Evolution of community trust, when the evaluation of competence and willingness changes
5.3 Citizens’ acceptance
In this section, we investigate the actual acceptance rate for the measures determined by P A. Such dimension is
subject to a sufficient trust evaluation, i.e. over a given intrinsic acceptance threshold σ. In order to investigate
this topic, we made use of the σ value reported in section 3.3.
Figure 2 shows the results of the simulation. It is easy to notice that this curve has a completely different
behaviour, with respect to trust. It is worth underlining that there is a strong difference between trust and
acceptance, although these two dimensions are strictly tied. The first represents a means the authority P A
has to convince the citizens accepting the restriction, while the second tells us the actual percentage of citizens
accepting them. Such a difference explains why we found limit values (maximal and minimal) for trust evaluation,
while this does not hold for acceptance rate, which varies from 0% to 99.6%, covering all the available interval.
Concerning the analysis of the corpus, subjects reported 89.72% acceptance rate. This high value actually
corresponds to what happened in the early stages of the pandemic.

6 Conclusions
Within this study, we proposed investigated the reaction of the Italian population to the first pandemic episode
of COVID-19, taking into consideration the strong link between this dimension and trust in Public Authorities.
This kind of analysis is particularly interesting, since it allows us to understand what happened in the past
months and, identifying what worked and what did not, it also offers useful suggestions on how to manage
further institutional interventions in the future. Most notably, the key conceptual advantage of this research
is the attention to the different trust dimensions, since trust is a complex and layered construct, with its
own internal dynamics. The core idea, widely highlighted in literature [Sie14, Lew20], is that trust in public
authorities is pivotal, both before and during the pandemic, to positively influencing people’s willingness to
adopt recommended behaviour.

The first result of this study confirms a definitely high trust in public institutions, equal to 72.35%. Consid-
ering the historical distrust in the Italian institutions, this is a surprising result. Although such value is very
different from what we would have expected, many other studies confirm such strong change. For instance, a
survey on a representative sample of Italian citizens (N = 1028, 16–17 March 2020), conducted in the same period

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Figure 2: Acceptance rate within the population

by the independent research centre Demos and Pi (http://www.demos.it/a01705.php), reported that 71% of
citizens trust both the Italian government and the current prime minister, compared to the 44% of the previous
month, and 94% approval of the adopted measures. Moreover, according to the analysis conducted by Statista
(https://www.statista.com/statistics/977223/support-for-prime-minister-conte-in-italy/), trust
in the Italian prime minister in February was around 39%. The same report shows a sudden rise of trust up to
54% immediately after the emergency arrival.

Therefore, we considered the evolution of trust when its components change, investigating what could have
happened or could still happen in the future in the presence of different conditions. Specifically, we are interested
in two specific dimensions: competence and willingness. Results revealed that, depending on these two features,
the maximum trust value was 82.14%. Such value represents a useful term of comparison, giving us a clearer
view of how high trust was during the pandemic. Conversely, the minimum trust value is estimated at 22.78%.

Remarkably, we identified a considerable difference in weight between competence and willingness. This kind
of phenomenon can be traced back to different factors. For example, it is possible that willingness is taken for
granted: given the specific role P A holds within the society, citizens may assume that it is certainly willing to
protect the population or that it is its responsibility to take care of such problems. Moreover, this lower weight
may be the consequence of the assumption that there are no secondary goals behind the actions of the authority
(creating alarmism, speculating on the crisis). Once malevolent reasons are excluded, the only possibility is
that the authority is actually determined to protect the population. Besides that, concerns about competence
need to be verified: weather P A is able to determine adequate and effective rules; if it made appropriate use of
experts contribution (e.g. epidemiologists, virologists, paediatricians, psychologists, etc.) and so forth. In such
a view, competence automatically plays a critical role.

The most noteworthy outcome of this work concerns the actual acceptance of the restrictive measures by
the population. We care to deepen this concept and to provide some clarification, as this is a key point in our
framework. It is important to underline that the final goal of the public authority is not just to increase trust,
but to increase the acceptance rate of the restrictive measures. For instance, the authority may estimate that
an acceptance rate of 80% would ensure an adequate response to the pandemic. From this perspective, a simple

9
trust assessment would not be a useful index, as different acceptance rates may correspond to a single trust
value.
In other words, every trust value enough high to exceed the acceptance threshold σ is fine. Although there is
a strong interconnection, trying to maximize trust does not necessarily coincide with maximizing the acceptance
rate too. Referring to the first pandemic episode, we estimate an average trust value of 72.35%, while the
acceptance rate is definitely higher (89.72%). Although this is an excellent result, we estimate that, under
specific circumstances, even a 60% trust value may be enough to convince more than 90% of the population.
High trust values may be interpreted as a positive signal, but it is not necessary to reach such values and they
would not be a guarantee of success.

In conclusion, this work, besides providing a detailed analysis and interpretation of what happened during
the first pandemic episode, could be of great help to understand and explain the decision-making processes of
the citizens even in future scenarios, guiding health authorities in providing informed interventions and clear
communications.

As future work, we would like to apply the category concept within this framework. We believe that the
findings of this work are already interesting. Nevertheless, understanding pandemics requires fine-grained data
representing specific local conditions and the social reactions of individuals, in order to cover the full behavioural
and social complexity of societies under pandemic crisis. Considering different social categories may provide a
more detailed perspective on the problem.

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