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\begin{document}

\title{Integrating agent-based modelling and behavioural data analytics: A case
study of climate change farmers' perception in Italy.}



\author[1,1]{Sandra Ricart}%
\author[1,1]{Paolo Gazzotti}%
\author[2,2]{Claudio Gandolfi}%
\author[1,1]{Andrea Castelletti}%
\affil[1]{Politecnico di Milano}%
\affil[2]{Università degli Studi di Milano}%


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  \date{May 27, 2024}


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\begin{abstract}
Climate change is arguably the most severe and complex challenge facing
today's society, a cross-cutting issue affecting many sectors and
connected to other global challenges, such as ensuring sustainable water
management and food security. Agricultural systems are adversely
influenced by climate change through increased water stress, change in
run-off patterns, seasonality fluctuation, and temperature variations.
Farmers are, hence, a valuable source of first-hand observations of
climate change as they may provide a deeper understanding of their
manifestation, relevance, and effects. Social and behavioural sciences
have investigated the influence of farmers' experiences in increasing
climate change adaptation capability and improving decision-making
processes at the system level. The conclusion is that local perceptions
provide sufficient baseline information for understanding individual and
collective exposure to climate risks, an essential element for effective
policy formulation and implementation. Traditional management approaches
based on simple, linear growth optimization strategies, overseen by
command-and-control policies, have proven inadequate for effective
adaptation to climate change. Conversely, accurate bottom-up approaches
focused on social learning can complement the system transformation by
building collaborative problem solving among individuals, stakeholders,
and decision-makers. In this context, deepening social perception
becomes fundamental for two main reasons: i) it is a key component of
the socio-political context, and ii) it is an essential step for
behaviour transformation and attitude change. In this line, associative
processing methods, such as interviews and surveys, have been discussed
for their ability to monitor the nature, extent, significance, and
influence of personal experience on climate change adaptation. Also,
modelling techniques have been recognized in social sciences as
effective mechanisms to simulate the social influence in decision-making
processes. System dynamics (e.g., causal loop diagrams, CLD) and
Agent-Based Models (ABM) can include feedback between social and
physical environments, define individuals' and stakeholders' narratives,
and map the social network with agents' interactions. This proposal aims
at testing how qualitative data can enable policy-makers and managers to
understand and re-think water management and climate change policies at
the local level, which is essential to address agricultural risks. From
a system dynamics approach, we examine how ABMs can most effectively
integrate behavioural data collected from semi-structured interviews and
surveys to increase robustness in decision-making processes while
attending to farmers' behaviour on climate change adaptation. We
surveyed 460 farmers and semi-structured interviews with 13 irrigation
consortiums from northern Italy to deepen a triple loop analysis on
climate change awareness, perceived impacts, and adaptive capacity.%
\end{abstract}%



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