=Paper=
{{Paper
|id=Vol-3182/paper16
|storemode=property
|title=Exploring the Boundaries between Law, ABM and Policy-making: on the Clash between Formal and Informal Norms
|pdfUrl=https://ceur-ws.org/Vol-3182/paper16.pdf
|volume=Vol-3182
|authors=Margherita Vestoso,Ilaria Cecere
|dblpUrl=https://dblp.org/rec/conf/jurix/VestosoC21
}}
==Exploring the Boundaries between Law, ABM and Policy-making: on the Clash between Formal and Informal Norms==
https://ceur-ws.org/Vol-3182/paper16.pdf
Exploring the intersections between law, ABM and policy-
making: on the clash between formal and informal norms
Margherita Vestoso 1,2, Ilaria Cecere 2
1
University “Federico II” - Dept. of Law, 80133, Napoli, Italy.
2
University of Sannio, Dept. of Law, Economics, Management, Quantitative Methods, 82100, Benevento, Italy
Abstract
The work focuses on the potential intersections between ABM and policy-making from a legal
perspective. After a brief introduction discussing the need for both policy-making and law to
gain a better understanding of social reality, we dwell on an emerging legal research
perspective, a sort of computational-enhanced legal empiricism, suggesting to use agent-based
simulation to enlighten complex social dynamics behind legal phenomena. The discussion is
supported by cues collected during a research experience exploiting agent-based simulation to
explore an issue that heavily impacts law and policy effectiveness, i.e., the clash between
formal and informal norms. In the last part of the work, we sketch some final considerations,
discussing ideas and methodological issues that emerged from the research experience that can
contribute to the reflection on the intersection between ABM, policy-making, and law.
Keywords 1
Rule-making, Computational Legal Empiricism, Informal norms, Social simulation
1. Introduction
Public policy design has always been a demanding process, oriented to identifying strategies that could
decrease the uncertainty of social reality and lead the community toward desirable outcomes. The
complexity of this process has increased today. From data revolution to worldwide health threats to
social media misinformation, the factors that policies have to govern are numerous and ever-evolving,
putting decision-makers in front of dynamics challenging to identify, predict, and control. Policies
failures have thus become an even more concrete issue.
As outlined in [1], one of the "principal cause of policy failure [...] lies in the intellectual framework
in which a policy is conceived”, which often do not consider mental models, beliefs, societal norms,
and other contextual factors that influence people's behaviour. In such a scenario, a challenge is to
identify approaches that can foster better-contextualized policies enabling them to efficiently manage
society non-linear dynamics.
The discussion involves the law as well. Legal norms are the instrument of choice for implementing
public policies: they provide the means through which governments undertake their core functions.
They prescribe behavioural standards and the connected enforcement methods, set formal institutions
endowed with regulatory powers and the mechanisms through which regulatory institutions can be held
accountable for their actions [2]. There is, therefore, a connection between understanding the dynamics
behind legal norms and improving policy effectiveness.
The work looks in this direction, discussing some of the topics and methodological issues arising at
the intersection between ABM, law, and policy-making. Specifically, Section 2 focuses on the role of
agent-based simulation in the legal field by drawing attention to the ideas suggested in this sense by the
emerging perspective of computational legal empiricism.
AMPM’21: First Workshop in Agent-based Modelling & Policy-Making, December 8, 2021, Vilnius, Lithuania
EMAIL: margherita.vestoso@hotmail.it (M. Vestoso)
© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Wor
Pr
ks
hop
oceedi
ngs
ht
I
tp:
//
ceur
-
SSN1613-
ws
.or
0073
g
CEUR Workshop Proceedings (CEUR-WS.org)
Section 3 extends the reflection by dwelling on a recent research experience combining behavioural
experiments and agent-based simulation to explore an issue that heavily impacts law and policy
effectiveness, i.e., the clash between formal and informal rules. The idea is not as much to discuss
specific results as to shed light on cues and methodological issues that emerged from the research
experience that can contribute to the reflection on the intersection between ABM, policy-making, and
law.
2. ABM and the empirical evolution of the legal science
Widely spread in many research fields connected to public policy [3, 4, 5], ABM remains a rather
unexplored frontier in the legal field [6, 7]. While interested in the intersection between law and at
Computational Social Sciences (CSS) approaches [8, 9], quantitative legal research has indeed paid
more attention to the idea of using computational approaches to investigate formal characteristics of
law rather than its factual dimension [10].
However, interesting perspectives have arisen in recent years that rethinks the relationship between
computational methods, quantitative research and legal empiricism. In particular, a view is emerging, a
sort of computational-enhanced legal empiricism, that suggests looking at CSS methods as enabling
factors for an evolution in the empirical sense of the way legal phenomena are conceptualized and
studied [11,12].
Indeed, the interdisciplinary paradigm of CSS has endowed social scientists with completely new
theoretical and methodological instruments allowing to gain deeper insights into the complexity of
social systems [9]. Computational-enhanced legal empiricism looks at such an evolution of social
science as an opportunity to rethink the methods and, even before, the theoretical models of legal
research. The idea suggested is that of “exploiting computation not only to identify trends and
correlations in case law by means of statistical regressions and machine learning, but also to investigate
other aspects of the legal phenomenon, like the intricate networks of cognitive and social mechanisms
through which law emerges, is applied, and exerts its effects” [11].
In this vein, a particular role is played by agent-based simulations, as they provide a powerful
formalism to experimentally explore the complex dynamics that govern social reality, enabling
researchers to generate social systems artefacts that can be observed, evaluated, and tested on a
computer. Inspired by a bottom-up approach, they make it possible to see how macro-level phenomena
(e.g., fundamental social structures or group behaviours) emerge from the micro-interactions taking
place among individuals governed by a simple set of rules and interplaying with an artificial
environment [13].
The approach helps unveil the eccentric nature of social interactions. Unlike more traditional
analytical solutions and approximation techniques, agent-based simulations allow catching nonlinearity
and feedback effects involved in social phenomena emergence [6, 13]. The observer is so able to see
how local interactions affect collective responses and, vice versa, how these affect individual choices
and local interactions.
The list of issues to analyse by this approach is long and includes mechanisms such as the cognitive
processes and spontaneous collective dynamics involved in the evolution of normative behaviours,
cooperation or social dilemmas. Mechanisms that are all relevant for policy and rules design and,
however, have never been explored, in the legal field, by means of the agent-based approach.
3. A practical challenge: simulating the clash between formal and informal
norms
Drawing inspiration from the theoretical and methodological approach suggested by computational-
enhanced legal empiricism, we present a research experience exploiting agent-based simulation to
explore a phenomenon that can heavily affect the effectiveness of public policies, i.e., the clash between
formal and informal rules [15]. Along with legal norms, indeed, collective behaviours are often affected
by moral, social or other informal rules that influence how individuals make decisions, build their
personal relationships or interact with groups they are part of. Typically unspoken, such norms work as
shared models of action setting the cultural and structural bases of human behaviour and so contributing
to the organization of social life [16, 17].
To understand the relevance of these rules we can refer to the image provided by the social
philosopher Cristina Bicchieri in [16]. She metaphorically describes social norms – an instance of
informal norms – as the “grammar of society”: like grammar rules shaping language and allowing us to
recognize it, informal norms shape communities' behaviour, making it possible to identify the inner
structure of social groups.
Just think, for example, of those cultures in which families withdraw girls from schools and marry
them as children. One factor in stopping girls’ education is the common belief in the need to protect
girls from honour threats they can receive when walking to or from the school. A social norm that
prevails on policies about the school, making them unable to cope with the problem of poor feminine
education effectively [18].
Even if grounded on different bases, formal and informal norms are not disconnected and, as
suggested by the example, they can easily be in conflict. The inner, uncoded rules of a community can
indeed hinder or completely exclude co-existing ones imposed by formal authorities, especially when
not neutral or in line with them.
The topic is not new. The impact of informal norms on social behaviour is a well-known topic of
investigation in social sciences [19, 20]. However, thanks to CSS approaches and, in particular, to ABM
new insights have been drawn concerning the dynamics underlying the spontaneous emergence of
informal norms [21].
Our reflection on these issues is supported by a recent research experience that uses agent-based
simulation to explore the conflict between formal and informal norms in the scenario of railway
maintenance. The contexts in which unspoken rules threaten the effectiveness of l formal ones are
countless. Due to their socio-technical nature, however, railway systems offer a privileged point of
view.
In recent years, indeed, innovation has strongly transformed the relationship between social, cultural,
technological and organizational factors characterizing these systems. Changes concern not only the
physical infrastructure but also operational rules. Such rules, as sophisticated as strategic in terms of
railway safety, made staff’s decisions - especially rail track maintainers – tough and increased the
application of shortcuts and unofficial rules [22], unsuitable for the complexity of the task and able to
fosters rail accidents [23].
Our work aims to explore the dynamics behind this phenomenon, trying to catch how different
dimensions (individual, collective, and organisational) involved interact. Drawing inspiration from
recent studies on group norms in the railway context [24, 25], we tried to understand whether
conformism can play a role in increasing rail maintainers’ use of unofficial practices.
The research takes a twofold direction. On the one hand, we attempted to understand how
conformism can influence rail operators’ decision-making when combined with typical conditions
affecting their work, such as time pressure and misinformation (caused by non-expert narratives). On
the other, we explored the macro effects of previously detected individual propensity to conformism,
analysing the impact of both time pressure and misinformation on the emergence of a trend favouring
informal norms.
4. Contents of the experimental activity
In order to catch the interplay between individual decision-making and collective dynamics involved
in the emergence of norms conflict in railway scenario, we integrated agent-based simulation with a
behavioural experiment based on real subjects. In the next sub-section, we report in detail the contents
of both the experimental activities.
4.1. Behavioural experiment
To understand if and how time pressure and misinformation could impact an individual's propensity
to conform to others' choices, we conducted a social experiment based on a revisitation of the protocol
adopted by Asch in his experiments on conformism [26, 27]. We asked each of 28 experimental subjects
(in 19-39 years old) to participate in a visual-perceptual experiment with other people (actually, actors
instructed by the experimenter to provide specific answers). All subjects have been asked to perform
18 trials by comparing some lines shown on a 55-inch screen. The stated objective of the observation
is to determine which one of the bars on the right side of the screen matches the comparison bar on the
left side.
However, as in the original experiment, there is a non-stated objective: to observe the impact of
actors' answers on the experimental subject’s opinion. For this reason, the test includes 12 critical trials
in which the actors unanimously provide a wrong answer, unbeknownst to the experimental subject (see
Figure 1). As in Asch, the number of failed critical trials (i.e., the number of times the subject follows
actors' wrong answer) describes the conformity rank of the subject, the variable to measure through the
experiment. We tried to explore the course of this variable in different scenarios by alternately adding
the following conditions: i) time pressure caused by time-limited (0,3') observation of trials on the
screen; ii) misinformation, by the sharing of false pieces of knowledge by one of the actors who
pretended to be an expert of visual experiments; iii) both time pressure and misinformation
Figure 1: Set of visual-perceptual trials. Numbers in red correspond to critical trials
4.2. Agent-based simulation
An example of the Figure 1, After we shed light on how factors such as time pressure and
misinformation can affect an individual's propensity to conformism, we used agent-based simulation to
grasp the impact of such factors on the emergence of a collective trend of using unofficial rules. In this
vein, we develop a model in NetLogo including a population of agents endowed with a 30% probability
to conform to the majority and no predefined preference for informal norms. Every tick of the
simulation is split into two steps. During the first one, five random agents called to fix a fault have to
decide if they agree about solving the problem by using informal practices rather than official rules. If
all agree, the group will breach the protocols and use shortcuts; otherwise, they will comply with official
rules. The agreement depends on if there are shortcuts' supporters in the group and on the probability
(30%) for those at odds to conform with the majority. After they decide, they fix the fault and join the
other agents.
Then, the simulation runs to the second step, where agents, back to the headquarter, discuss the value
they assigned to shortcuts over the intervention with short-distance colleagues. To test the impact of
misinformation on the diffusion of informal norms, we explored different information-sharing
conditions. Specifically, we observed how the use of shortcuts changes in combination with the
following scenarios: i) agents with no experience in using shortcuts share information about them and
their usefulness; ii) agents share information about shortcuts and their usefulness only if they have
previously used them; iii) agents cannot share information about informal norms.
An illustration of the outcomes produced by the comparison of the scenarios can be observed in
Figure 2.
Figure 2: Distribution of informal norms among agents in the following scenarios: (a)
misinformation; (b) no misinformation.
5. Discussion and conclusive remarks
The research experiment outlined in the previous section has raised issues worthy of attention for
those who are interested in exploring the potential intersections of ABM, policy-making, and law. A
first set of remarks concerns social dynamics implicated in the clash between formal and informal
norms. We have observed the important impact that conformism can have on spreading informal norms
and how this impact can be extended by misinformation. As emerged from the agent-based simulation
(see Figure 2), when non-experts can share information about informal norms, the preference for these
norms rises and so does the number of times in which agents use them. We would suggest that in this
scenario agents have more chances for discussion exchanges about informal rules and, consequently,
more opportunities to conform.
The next step of the research includes an improvement of the simulation model, both from a
theoretical and technical standpoint, to extend the exploration to other cases of conflicts between formal
and informal norms. The idea is to investigate if such a correlation between misinformation and
informal norms’ diffusion is a context-related or a class feature, i.e., if it occurs even in other kinds of
formal-informal norms’ conflict.
A second set of remarks is on the methodological level and relates to the need to nurture
interdisciplinary and empirical approaches in the legal field by gaining a natural "computational mental
habit." Computational-enhanced legal empiricism, as mentioned, is moving in this direction, suggesting
to look at computation as a means that can innovate not only methods but the very culture of legal
research, changing how we identify, theorize, and study legal phenomena [11].
Fiddling with ABM led us to directly experience such mutual influence between the methodological
choices and the theory-making process. We observed, in fact, how changes in the model enable a
different way to think of the phenomenon, a new theory, and vice-versa how different representations
affect the modelling choices and the related research questions. Along with potentialities, however, we
experienced also the limits caused by the lack of computational culture: basic technical skills are indeed
required to grasp all the advantages agent-based simulation and other computational social science
methods can bring to understanding social reality phenomena. A deep exploration of the intersections
between ABM, law and policy-making thus asks legal scholars to become familiar with new tools,
languages, and problem formalization processes. The knowledge we need not only to develop better
models but grow new scientific interplays.
6. Acknowledgements
The authors are sincerely grateful to Prof Nicola Lettieri for his precious teachings and the
theoretical contribution provided to this work.
7. References
[1] F. Squazzoni. A social science‐inspired complexity policy: Beyond the mantra of incentivization.
Complexity 19(6) (2014): pp. 5-13.
[2] E. L. Windholz. Governing through regulation: public policy, regulation and the law. Routledge,
2017.
[3] L. Hamill, G.N. Gilbert. Agent-based Modelling in Economics, Wiley, Chichester, 2016.
[4] R.C. Picker. Simple Games in a Complex World: A Generative Approach to the Adoption of
Norms. Univ Chicago L Rev 64(4) (1997): 1225–88.
[5] C.K Frantz. S. Siddiki. Institutional Grammar 2.0: A specification for encoding and analyzing
institutional design, volume 99 of Public Administration, (2021).
[6] S. Benthall, K.J. Strandburg. Agent-Based Modeling as Legal Theory Tool. Frontiers in Physics
(9) 2021: 337.
[7] A. Schwartz. An Agent-Based Model of Judicial Power. JL 9 (2019): 21.
[8] C. Cioffi-Revilla. Introduction to computational social science. London and Heidelberg: Springer,
2014.
[9] R. Conte, N. Gilbert, G. Bonelli, C. Cioffi-Revilla, G. Deffuant, J. Kertesz, V. Loreto, S. Moat,
J.P. Nadal, A. Sanchez, A. Nowak, A. Flache, M. San Miguel, D. Helbing. Manifesto of
computational social science. The European Physical Journal Special Topics, 214 (1) (2012): 325-
346.
[10] M.C. Suchman, E. Mertz.Toward a new legal empiricism: empirical legal studies and new legal
realism. Annual Review of Law and Social Science 6 (2010): 555-579.
[11] N. Lettieri. Law in the Turing’s Cathedral. Notes on the Algorithmic Turn of the Legal Universe,
in: W. Barfield (Ed.), The Cambridge Handbook of the Law of Algorithms. Cambridge University
Press, 2020, 691-721.
[12] N. Lettieri. Computational social science, the evolution of policy design and rule making in smart
societies. Future internet 8(2) (2016): 19.
[13] J. M. Epstein, R. Axtell, Growing Artificial Societies: Social Science From the Bottom Up. The
MIT Press, Cambridge, 1996.
[14] J. M. Epstein. Generative Social Science: Studies in Agent-Based Computational Modeling.
Princeton University Press, 2006.
[15] K. Nyborg, J.M. Anderies, A. Dannenberg, T. Lindahl, C. Schill, M. Schlüter, A. De Zeeuw. Social
norms as solutions. Science, 354(6308) 2016: 42-43.
[16] C. Bicchieri, The grammar of society: The nature and dynamics of social norms. Cambridge
University Press, 2006.
[17] M. Xenitidou, B. Edmonds, B., (Ed.), The complexity of social norms. Springer, New York, NY,
2014.
[18] B. Cislaghi, E.K.Denny, M. Cissé, P. Gueye, B. Shrestha, P. Shrestha, G. Ferguson, C. Hughes, C.
J. Clark. Changing social norms: the importance of “organized diffusion” for scaling up
community health promotion and women empowerment interventions. Prevention Science 20(6)
2019: 936-946.
[19] R.C. Ellickson, Order without Law: How Neighbors Settle Disputes. Harvard University Press,
1991. https://doi.org/10.2307/j.ctvk12rdz.
[20] M. Sherif. The psychology of social norms. Harper and Row, 1st ed., Oxford (1966)
[21] U. Lotzmann, M. Möhring, K.G. Troitzsch. Simulating the emergence of norms in different
scenarios. Artificial intelligence and law 21(1) (2013): 109-138.
[22] R. Lawton. Not working to rule: understanding procedural violations at work. Safety science, 28(2)
(1998): 77-95.
[23] Rail Accident Investigation Branch (RAIB). Report 11/2018: Near miss with a group of track
workers at Egmanton level crossing, Nottinghamshire, 5 October 2017.
[24] J.M. Sanne. Framing risks in a safety‐critical and hazardous job: risk‐taking as responsibility in
railway maintenance, Journal of Risk Research, 11(5) (2008): 645-658.
[25] K.M. Kura. Mohd Shamsudin, F., & Chauhan, A. Modeling the Influence of group norms and self-
regulatory efficacy on workplace deviant behaviour. Asian Social Science, 9(4) (2013): 113-122.
[26] S.E.,Asch. Effects of Group Pressure on the Modification and Distortion of Judgments. In
Guetzknow, H., (Ed.), Groups, Leadership and Men, Carnegie Press, Pittsburgh, PA, 1951:177-
190.
[27] S.E. Asch, Studies of independence and conformity: A minority of one against a unanimous
majority, Psychological monographs: General and applied, 70(9), (1956): 1-70.