%% C2S 2026 Keynote — Simon Carrignon
%% Computational Cultural Science Workshop, Paris, 18--19 May 2026
%% École nationale des Chartes -- PSL
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    \tiny Simon Carrignon · UCL GEE \hfill
    C2S Workshop, Paris · 18--19 May 2026 \hfill
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%% ── Title info ───────────────────────────────────────────────────────────
\title{Bridging Scales in Cultural Evolution}
\subtitle{Agent-Based Modelling, Archaeological Patterns,\\and Computational Approaches to Human Cultural Change}
\author{Simon Carrignon}
\institute{%
  Department of Genetics, Evolution \& Environment\\
  University College London\\[4pt]
  \small CDAL · ENCOUNTER · UGI
}
\date{%
  C2S — Computational Cultural Science Workshop\\
  École nationale des Chartes -- PSL, Paris\\
  18--19 May 2026
}

%% ═══════════════════════════════════════════════════════════════════════════
\begin{document}

\maketitle

%% ── Outline ──────────────────────────────────────────────────────────────
\begin{frame}{Outline}
  \tableofcontents
\end{frame}

\section{The Problem: Scales and Data}

%% ── Frame 1 ──────────────────────────────────────────────────────────────
\begin{frame}{Computational Methods Have Transformed Cultural Research}
  \begin{columns}[T]
    \begin{column}{0.55\textwidth}
      \textbf{What we can now do:}
      \begin{itemize}
        \item Mine large cultural datasets (texts, images, artefacts)
        \item Extract meaningful patterns without prior theory
        \item Apply ML, Bayesian inference, network analysis,\\dimensionality reduction
      \end{itemize}
      \vspace{6pt}
      \textbf{Result:} unprecedented empirical reach
    \end{column}
    \begin{column}{0.42\textwidth}
      \begin{block}{Key methods}
        \small
        Machine learning\\
        Bayesian inference\\
        Network analysis\\
        Dimensionality reduction\\
        Agent-based modelling
      \end{block}
    \end{column}
  \end{columns}
\end{frame}

%% ── Frame 2 ──────────────────────────────────────────────────────────────
\begin{frame}{But There Are Limits}
  \begin{alertblock}{The bias problem}
    These methods remain biased toward \textbf{certain types of data}
    and \textbf{certain scales of analysis}
  \end{alertblock}
  \vspace{10pt}
  \begin{itemize}
    \item Most work focuses on \emph{large, digitised, recent} corpora
    \item Societies without extensive textual records are underrepresented
    \item Local patterns ≠ global mechanisms
  \end{itemize}
  \vspace{10pt}
  \begin{exampleblock}{The challenge}
    How do we understand \textbf{general human cultural behaviour at scale},\\
    including societies that have left \textbf{few easily interpretable traces}?
  \end{exampleblock}
\end{frame}

%% ── Frame 3 ──────────────────────────────────────────────────────────────
\begin{frame}{The Mesoscale Gap}
  \begin{center}
    \begin{tikzpicture}
      \draw[->, thick, pslblue] (0,0) -- (10,0) node[right] {\textbf{Scale}};
      % Local
      \node[draw, rounded corners, fill=psllight, text width=2cm, align=center] 
        at (1.5,1.2) {\small Local\\interactions};
      % Meso
      \node[draw, rounded corners, fill=pslgold!30, text width=2.5cm, align=center,
        thick, pslgold] 
        at (5,1.8) {\textbf{Mesoscale}\\Regional\\Millennial};
      \node[below=2pt] at (5,0.8) {\footnotesize \textit{our target}};
      % Global
      \node[draw, rounded corners, fill=psllight, text width=2.2cm, align=center] 
        at (8.5,1.2) {\small Global\\patterns};
      % Arrows
      \draw[->, midgray] (2.5,1.2) -- (3.8,1.5);
      \draw[->, midgray] (7.2,1.2) -- (6.2,1.5);
    \end{tikzpicture}
  \end{center}
  \vspace{4pt}
  \begin{itemize}
    \item Processes unfolding at \textbf{regional level over millennia}
    \item Mechanisms observed locally must be \textbf{extrapolated} carefully
    \item Evidence is often \textbf{archaeological} — fragmented, biased, indirect
  \end{itemize}
\end{frame}

\section{The Approach}

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\begin{frame}{Our Proposal: Coupling Data and Models}
  \begin{center}
    \begin{tikzpicture}[node distance=3.5cm]
      \node[draw, rounded corners, fill=pslblue!15, text width=3.2cm, align=center, 
        minimum height=1.5cm] (data) 
        {\textbf{Archaeological\\Data}\\[4pt]\small ML · Bayesian\\pattern extraction};
      \node[draw, rounded corners, fill=pslgold!25, text width=3.2cm, align=center, 
        minimum height=1.5cm, right of=data] (model) 
        {\textbf{Agent-Based\\Models}\\[4pt]\small Cultural change\\exploration};
      \node[draw, rounded corners, fill=accent!15, text width=3cm, align=center, 
        minimum height=1.5cm, right of=model] (hypo) 
        {\textbf{Hypothesis\\Testing}\\[4pt]\small Mesoscale\\predictions};
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        (data.south) node[midway, below] {\small feedback};
    \end{tikzpicture}
  \end{center}
\end{frame}

%% ── Frame 5 ──────────────────────────────────────────────────────────────
\begin{frame}{Why Agent-Based Modelling?}
  \begin{columns}[T]
    \begin{column}{0.5\textwidth}
      \textbf{ABM allows us to:}
      \begin{itemize}
        \item Model \textbf{individual decisions} and local interactions
        \item Observe \textbf{emergent} population-level patterns
        \item Test hypotheses where \textbf{controlled experiments} are impossible
        \item Explore \textbf{counterfactuals} and sensitivity
      \end{itemize}
    \end{column}
    \begin{column}{0.47\textwidth}
      \begin{block}{Epstein (2008)}
        \small ``The agent-based computational model is a new tool for social science\ldots it generates candidate explanations.''
      \end{block}
      \vspace{6pt}
      \begin{block}{Levins (1966)}
        \small Models trade off generality, realism, and precision. Choose deliberately.
      \end{block}
    \end{column}
  \end{columns}
\end{frame}

\section{Case Studies}

%% ── Frame 6 ──────────────────────────────────────────────────────────────
\begin{frame}{Case Study 1: Cultural Diversity and Taphonomic Bias}
  \begin{itemize}
    \item \textbf{Question:} How does archaeological preservation bias our view\\of past cultural diversity?
    \item \textbf{Approach:} Network-based community detection + ABM
    \item \textbf{Data:} Bronze Age assemblages (BIAD / EPNet datasets)
    \item \textbf{Method:} Simulate cultural exchange networks,\\apply taphonomic loss, compare to empirical patterns
  \end{itemize}
  \vspace{8pt}
  \begin{exampleblock}{Key finding}
    Spatial structure and mobility rates interact to determine\\
    how much diversity is \emph{observable} vs. \emph{lost to the record}
  \end{exampleblock}
\end{frame}

%% ── Frame 7 ──────────────────────────────────────────────────────────────
\begin{frame}{Case Study 2: Patterns at Regional Scale}
  \begin{itemize}
    \item \textbf{Question:} Can we extract meaningful cultural signals\\from heterogeneous archaeological datasets?
    \item \textbf{Approach:} Machine learning + Bayesian classification\\of artefact assemblages
    \item \textbf{Tools:} Dimensionality reduction (PCA, UMAP),\\Bayesian mixture models
    \item \textbf{Coupling:} Patterns inform ABM parameter spaces\\for cultural transmission models
  \end{itemize}
  \vspace{8pt}
  \begin{alertblock}{The challenge of the record}
    Archaeological evidence is fragmentary, spatially biased,\\
    and temporally coarse — methods must account for this explicitly
  \end{alertblock}
\end{frame}

%% ── Frame 8 ──────────────────────────────────────────────────────────────
\begin{frame}{Case Study 3: Modelling Cultural Change Trajectories}
  \begin{itemize}
    \item \textbf{Question:} What cultural transmission processes\\generate the patterns we observe?
    \item \textbf{ABM setup:} Agents represent social groups;\\traits spread via biased transmission, drift, migration
    \item \textbf{Validation:} Simulated distributions compared to archaeological record
    \item \textbf{Scale:} Regional networks over centuries to millennia
  \end{itemize}
  \vspace{8pt}
  \begin{block}{Why this matters for C2S}
    The same pipeline applies to \textbf{textual/cultural corpora}:\\
    extract patterns → model mechanisms → test hypotheses
  \end{block}
\end{frame}

\section{Perspectives}

%% ── Frame 9 ──────────────────────────────────────────────────────────────
\begin{frame}{Connecting to Computational Cultural Science}
  \begin{columns}[T]
    \begin{column}{0.48\textwidth}
      \textbf{From archaeology to culture broadly:}
      \begin{itemize}
        \item Same questions, different data types
        \item Texts, images, media as ``cultural assemblages''
        \item Disruption metrics (Kim et al. 2026)\\as cultural fitness proxies
        \item NLP + ABM for media dynamics
      \end{itemize}
    \end{column}
    \begin{column}{0.48\textwidth}
      \textbf{Open challenges:}
      \begin{itemize}
        \item Model validation without ground truth
        \item Integrating heterogeneous data types
        \item Interpretability of ML-extracted patterns
        \item Theory–data feedback loops
      \end{itemize}
    \end{column}
  \end{columns}
\end{frame}

%% ── Frame 10 ─────────────────────────────────────────────────────────────
\begin{frame}{Summary}
  \begin{enumerate}
    \item Computational methods have \textbf{transformed} cultural research\\
          but remain biased toward certain data and scales
    \vspace{6pt}
    \item The \textbf{mesoscale} — regional processes over millennia —\\
          is tractable by coupling ML/Bayesian extraction with ABM
    \vspace{6pt}
    \item \textbf{Archaeological evidence} is a hard test case:\\
          methods that work here generalise
    \vspace{6pt}
    \item This pipeline connects directly to broader \textbf{computational\\
          cultural science}: patterns → mechanisms → predictions
  \end{enumerate}
\end{frame}

%% ── Thank you ─────────────────────────────────────────────────────────────
\begin{frame}[standout]
  \centering
  \Large Thank you\\[12pt]
  \normalsize
  \href{mailto:s.carrignon@ucl.ac.uk}{s.carrignon@ucl.ac.uk}\\[6pt]
  UCL GEE · CDAL · ENCOUNTER · UGI\\[10pt]
  \small
  Slides \& code: \href{https://github.com/simoncarrignon}{github.com/simoncarrignon}\\[16pt]
  \textit{Computational Cultural Science Workshop}\\
  École nationale des Chartes -- PSL · Paris · May 2026
\end{frame}

%% ── Backup slides ─────────────────────────────────────────────────────────
\appendix

\begin{frame}{References}
  \small
  \begin{itemize}
    \item Epstein, J.M. (2008). Why Model? \textit{JASSS} 11(4):12.
    \item Levins, R. (1966). The strategy of model building in population biology.
          \textit{Am. Sci.} 54:421--431.
    \item Bonabeau, E. (2002). Agent-based modeling. \textit{PNAS} 99:7280--7287.
    \item Reichenbach et al. (2007). Mobility promotes and jeopardizes biodiversity.
          \textit{Nature} 448:1046--1049.
    \item Kim, Kojaku \& Ahn (2026). Uncovering simultaneous breakthroughs.
          \textit{Science Advances} 12(14).
  \end{itemize}
\end{frame}

\begin{frame}{The Archaeological Record: Key Challenges}
  \begin{itemize}
    \item \textbf{Preservation bias:} organic materials lost; hard materials persist
    \item \textbf{Spatial bias:} sampling effort uneven across regions
    \item \textbf{Temporal resolution:} centuries collapsed into single ``phases''
    \item \textbf{Inference gap:} behaviour → material culture → preservation → observation
  \end{itemize}
  \vspace{6pt}
  Methods must be \textbf{explicitly designed} to account for these filters,\\
  not applied naively from other domains.
\end{frame}

\end{document}