A Framework for Sensemaking

・6 min read

We all are trying to make sense of the world. For that, we implicitly or explicitly use frameworks: scaffolding in the form of conceptual spaces, ontologies, models, and theories that guide our perceptions, judgements, and predictions – and that can be more or less useful.Examples for generic sensemaking frameworks are Dave Snowden’s Cynefin and Cynthia Kurtzs’s Confluence framework. The Product Field is a domain-specific sensemaking framework.

Three Criteria and a Strategy

A sensemaking framework is more useful

While a framework is clearly better the higher it scores in all dimensions, wide scope/high detail/low load frameworks being the ones to look for, in reality these dimensions stand in tension with each other: Often a general framework that allows for a lot of detail is complicated and cognitively demanding; a general and easy-to-use framework usually allows for little detailed insight; and in most instances, a detail-rich framework can only cover a narrow scope if it is to stay comprehensible.The mentioned frameworks fall into the “general and easy-to-use” (Cynefin and Confluence) and “detail rich, narrow scope” (Product Field) categories.

A framework can score high in all three dimensions if it provides abstractions that can be used in a fractal manner:

Visualising these abstractions and embodying them in practice further reduces cognitive load by employing more cognitive capacities in parallel, while the incorporation of different senses and sensibilities reduces bias and increases input and thus available detail.

The following framework applies these strategies.

A Proposed Implementation

Theories about empirical reality (that exhibit an ought-relation from world to judgement – our judgements should conform to the world) work on different levels of abstraction, the “right” level for a given set of phenomena being the one where a theory provides a maximum of explanatory and predictive scope and detail. That is the case when a theory’s model of relationships between (atomic and emergent) components and attributes of a system delivers maximum informationSee Hoel (2017) and his concept of “causal emergence”.

about the system when compared to other descriptions.

A very abstract ontology based on systems that live in state spacesAn example for such an ontology is Deleuze’s abstract process ontology in the interpretation of DeLanda (2002).

is our best framework for describing the most fundamental level – “what there is” Quine (1948); contra Quine, this is a realist ontology!

. Everything in the universe, from subatomic particles to galaxy clusters, from emotions to economic systems, can in principle be understood and explained by causal and/or mathematical models based on and constrained by this ontologyFor a proposal of a theory based on such an ontology that describes models on multiple levels see Hesp et al. (2019) and in general that work around Karl Friston’s Free Energy Principle.

– including how and why these models and explanations have to be criticised. This maximises generality.

Within that framework, specific models describe specific regions of the universe with varying scope and detail. On a physical level, M-theory is as much such a model as were Newtonian physics or Platonic geometry.Through this lens, the “unreasonable effectiveness of mathematics” (Wigner 1960) becomes as un-mysterious as the prevalence of specific mathematical tools, like group theory, in modern theoretical physics: At its core, theoretical physics is an increasingly abstract description of increasingly intricate patterns in an increasingly large state space. Also, the specifics of these models and the accompanying theories suddenly become much less interesting if they are not expected to provide an ontology themselves anymore, but are only concretisations of it.

On other levels of abstraction, looking at wider and more complex systems, a neurophysiological explanation has more causal information (scope and detail) about brain structure or behaviour than a physical one; a memetic explanation of the ascendance of consumer capitalism might have more causal information than a historical one.

All of these systems can be represented in state spaces, modelling their emergent behaviour as following specific attractors determined by the interactions of their components. This means high score for generality, and provides potential for visualisation. From this approach, we can derive several fractal abstractions, for example:

Explicit or implicit moral theories (that exhibit an ought-relation from judgement to world – the world should conform to our judgements) are a different type of theory – they don’t (aim to) describe reality, but are constructive solutions to practical problems.See “Is there such a thing as moral truth?”

At the same time, we value in them the same qualities as in empirical theories: scope, specificity and ease-of-use.

Moral theories are developed and checked for validity (or usefulness) not from a third-, but from a first-person perspectiveSee “Perspectives”

, which itself (and with it the emergence of normativity) can be explained by an empirical, more specifically an evolutionary theorySeen again Hoel (2017). For another non-reductionist evolutionary account see Deacon (2011).

. This increases the latter’s generality and makes moral theories part of the overall framework.

Every theory is necessarily developed, judged and disseminated from a first-person perspective and in a specific social context. This perspective must be reflected on and, if necessary, criticised when making moral judgements, i.e. constructing solutions to practical problems. Not reflecting on this perspective or taking an explicit moral stance towards the context means implicitly taking an affirmative stance towards it. This highlights the importance of critical, e.g. feminist or post-colonialist theories.

With this framework, we get a non-reductionist naturalism that includes seemingly un-naturalistic perspectives without giving up on (and instead explaining) their normative power.Overall, this proposal can be read as an updated and rephrased version of a Sellarsian “fusion” of the Scientific and the Manifest Image (Sellars 1962).

It uses fractal abstractions on different levels of abstraction to increase generality and specificity while reducing cognitive load.



I’m grateful to Phil Harvey for comments on a draft of this essay.