(A copy of an essay assignment to the course “Philosophy of Science” in Aalto University)

1 The context of my research

Before inquiring into the notions of explanatory value and understanding, let me briefly provide my research context: Overarching the process is a theory of intergroup contact (Allport 1954; Brown and Hewstone 2005; Pettigrew and Tropp 2006). The theory and the field of research that stems from it study how contact: a meeting between members of conflicting social groups, could reduce prejudice and improve attitudes. My research, focusing on the Israeli-Palestinian conflict, explores technological and creative means for such a contact. First, I am using telerobotics as a medium – enabling a physical encounter between the groups without the logistic effort of bringing individuals to the same space (A. Peled, Leinonen, and Hasler 2020). Second, I use puppet theater as a collaborative and creative tool for expressing and dealing with social and political concerns (Avner Peled, Leinonen, and Hasler 2024a). Therefore, we could define the research as interdisciplinary – combining social sciences, human-computer interaction, and the arts.

2 Scientific research as Active Inference.

The capacity of science to explain reality is laden with logical and metaphysical challenges (Godfrey-Smith 2003), even more so in the social sciences (Risjord 2022). I propose an alternative view of scientific research that is more action-oriented than explanatory. We start by declaring the goal of scientific research not to provide a water-tight explanation of phenomena but to construct a model of the world that advances the survival of society. Explanation and understanding are thus tools by which the model is enriched. Additionally, insofar as the goal of societal survival is entangled with the survival of the earth and its ecosystem (Barad 2007), the model is not human-centered.

I propose a model based on “Active Inference” (Parr, Pezzulo, and Friston 2022). At its core, Active Inference is a framework for cognitive sciences and computation, but the theory and its underlying principle – The Free Energy Principle (FEP), have been explored as models for scientific research (Pietarinen and Beni 2021; Balzan 2021). FEP is an optimization approach for Bayesian inference – a popular statistical method for casual modeling (Risjord 2022). In Bayesian inference, empirical evidence is repeatedly assessed against an existing “prior belief model” (consisting of probabilities of events to occur given various parameters) and updated with the new evidence, forming a “posterior belief model”. The trouble with Bayesian inference is that assessing the fitness of a model to the evidence (its correspondence with reality) is infinitely complex when the model includes infinite parameters. This problem is referred to in the literature as the problem of “marginal likelihood” (Chan and Eisenstat 2015). It is somewhat analogous to the impossibility of providing a “thick description” (Geertz 2008) that describes all possible factors or interventionist counterfactuals (Woodward 2005) for all possible parameters.

Instead of attempting to devise complete analytical models of the world, Active Inference chooses actions that minimize “Free Energy” (Friston et al. 2023). Defined as complexity minus accuracy, the goal is to reduce the complexity of the model while increasing its accuracy. This is also described as minimizing “surprise” or “prediction errors”. Additionally, by evaluating “Expected Free Energy” (Millidge, Tschantz, and Buckley 2021), the decision-making algorithm in Active Inference chooses (in a balanced manner) actions that it expects would lead to gaining new information, increasing overall prediction accuracy and widen the spread of information. Karl Friston, the inventor of FEP, suggests that it is not just an arbitrary optimization method but a principle inherent to all living systems. A well-defined system (what Friston calls a “Markov blanket”) necessarily adapts to its surroundings to maintain its boundary and not dissipate into the environment; It does so by minimizing the prediction error of its actions (Kirchhoff et al. 2018). I suggest applying FEP to scientific research. The Markov blanket, in this case, is society as a whole, maintaining its survival by conducting science. Scientific research under Active Inference is not obligated to explain certain phenomena as long as it works toward minimizing the Free Energy of society [^1].

3 The state of intergroup conflict research

From the perspective of FEP, research that strives for a decrease in violence and conflict in society is productive. A system occupied with internal conflict and self-deprecation is not spending its energy on harmony and adaptation with the surroundings (as an anecdote, the discourse on climate change in Israel and Palestine is scarce (Roberts 2020)). Mass violence and war amount to an unproportioned decrease in diversity, robustness, and productivity – reducing the overall sustainability of society. Research in intergroup contact theory attempts to construct a model that reduces conflict. Typically, social science models are repeatedly contended, contradicted, and nuanced. That does not mean that research is without value. Every paper in contact research is another piece of a puzzle that increases the accuracy of some predictions and illuminates various concepts in conflict resolution, thereby reducing the complexity of the task at hand.

Nevertheless, in the current battle between the forces that drive group polarization (such as social media echo chambers driven by the human tendency to confirm existing beliefs (Knobloch-Westerwick, Mothes, and Polavin 2020)) and the forces that drive reconciliation (such as intergroup contact), it is apparent that the former forces are more potent. From a computational perspective, we could say intergroup contact research is at a local minimum. The research is making incremental progress but in too small steps compared to the negative direction in which society is heading. At this point, we need research that favors exploration on exploitation – research that, although slightly increases the complexity of the model, provides more pathways for action, discovering escape routes from existing paradigms.

4 A scientific trickster

As pointed out, my research began as an intersection of two disciplines. In the initial theoretical and survey work Avner Peled, Leinonen, and Hasler (2024b), we applied Human-Robot Interaction (HRI) theories to intergroup contact and vice-versa. We explained survey results by merging the two fields and later tested the resulting hypotheses in co-design workshops (Avner Peled, Leinonen, and Hasler 2024b). This kind of work amounts to an expansion of the field of action – an increase of model entropy toward the mitigation of conflict, along with a steady increase in the predictability of actions taken in this path. However, as I move closer to the end of the doctoral program, I embrace the position of standing at the crossing of two pathways as a strategic choice. In my latest telerobotic workshops with Israeli and Palestinian participants (Avner Peled, Leinonen, and Hasler 2024a), we used methods from the Theatre of the Oppressed by Augusto Boal (2008): a framework for involving non-actors in political theater. Boal introduces the role of the “Joker” – a workshop facilitator and trickster of sorts (Schutzman 2018). The Joker bends the rules, sketches out boundaries, crosses them, mediates, dissolves, and playfully and humorously tackles sensitive topics – All to enable meaningful social discourse through theater.

I see myself as a scientific trickster, alluding to the mythological role of tricksters as mischievous yet beneficial mediators (Hyde 1997). I am situated at the border of Art and Science, meditating and cherry-picking models from one to another and questioning the definitions of both. In our participatory workshops, we attempt to blur the lines between HRI researcher and user, theatre actor and spectator, and challenge the idea of national borders (with telerobotics). Importantly, we opened a “corridor of humor” – a concept articulated by trickster artist Marcel Duchamp (Weppler 2018). We used humor as a tool for nonlinear thinking, as the participants produced robotic puppet shows about the conflict. So, to answer the question: “How does my research promote understanding?”: In some cases, it is a linear expansion and progression of the societal model, unifying different theories in a single architecture. But above all, it is the meta-level understanding that science can be art, that art can be science, and that humor and play can be research. The analytical value is secondary to promoting the robustness and flexibility of the Free Energy model toward the survival of society on this planet.

References

[^1]: Granted, the question of what states are preferred for the survival of society is not trivial and is open for debate (see the concept of “prior preferences” in Active Inference (Parr, Pezzulo, and Friston 2022)).

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