The explanation for the complex, multi-scale structure of biological and social systems lies in their manipulation of space and time to reduce uncertainty about the future.

By: Jessica C. Flack

Date: April 2014

In: Santa Fe Institute Bulletin and From Matter to Life – Chapter 12

• Central premises:
  • Biological systems (order in space and time) form by processing information about regularities in the environment.
  • Individual components of biological systems perceive environmental regularities.
  • Individual perceptions may converge and a collective consensus about the “average value” of a regularity over time may emerge.
  • The consensus value may better reflect the regularity than the (noisy) fluctuations perceived by any one component.
  • Individual components may use the collective consensus view (based on the  past) to estimate the appropriate course of action (in the present) or make predictions (about the future).
  • This type of “information processing” by biological systems and their components leads to:
    • More efficient survival (maximum energy extraction and minimum uncertainty).
    • Faster adaptation (fine tuning of individual strategies, learning).
    • Increased complexity (energy is freed up to explore new paths).

Key Takeaways

• Priors, derived from experience of regularities over time, drive better prediction.
• Better prediction drives more efficient survival, faster adaptation and increased complexity.
• No regularities to “exploit” = no complex biological systems.
• Longer time scale of regularities, slower feedback: = higher required level of complexity to understand and adapt behavior (elaborate model needed for encoding).

Worth Reading

• The framework for collective cognition is helpful in exploring the formation of priors, hierarchy in the brain, learning, chunking, etc.
• Helps to understand our bias for using averages as our “slow variables” for better prediction (or more broadly, formation of most perceptual biases).
• Also points to difficulty of dealing with irregular, longer time scale, slow feedback regularities. It is more difficult for a consensus view to emerge when dealing with non-intuitive, non-linear, less predictable outcomes.
• Along the same lines, macro-properties (for instance, political institutions) deteriorate when they are no longer (or have never been) supported by cohesive consensus views. Hierarchical order can only be maintained when the consensus provides a “good enough” estimate for a sufficient number of individuals / components.

Key Concepts:

Biological systems: networks that manipulate space and time.

• Biological systems are networks.
  • Systems …
    • Composed of basic (heterogenous) elements, components.
  • … with inputs in the form of components that pursue strategies …
    • Rule-based interactions among components.
  • … and outputs in the form of collective behavior …
    • Functional properties that emerge at aggregate system levels.
  • … where feedback …
    • System (macro) properties feed back into component (micro) decision making and behavioral strategies.
  • … allows for adaptation.
    • Components (and the system) learn.
• Biological systems process information to improve prediction:
  • More efficient survival:
    • Maximize energy extraction.
    • Minimize uncertainty (order, low variance).
  • Faster adaptation:
    • Learn: fine-tuning behavioral strategies.
  • Increased complexity:
    • Free-up energy

Course-graining (subjective interpretation of regularity in the environment) leads to better prediction

• Biological systems exploit regularities in the environment …
  • Evolution is essentially the process of learning from and adapting to regularities in the environment.
  • Adaption requires regularities that can be estimated (and/or manipulated).
  • The goal of adaptation is fine-tuning strategies to maximize energy generation and minimize uncertainty.
• … as the perceptions of its individual components converge …
  • Each component perceives noisy, short time-scale, fluctuating regularities.
  • Over time, individual perceptions of components may converge.
• … a collective consensus may emerge …
  • About the average value of the perceived regularity (the “slow variable”).
• … that improves prediction …
  • The slow variable may be a better input for prediction than any noisy individual perception.
  • The slow variable operates on a slower time-scale than the noisy underlying interactions.
• … and leads to the formation of priors.
  • Slow variables become the subjective, inferential basis for the formation of priors: models of the environment.
  • Priors are hypotheses about the present and future environment that are induced from the past environmental states (collective perceived averages).
  • Priors are updated with observed regularities.
  • [In other words, “beliefs” are formed against which any new incoming data will be tested – see also “How to Change your Mind” and “REBUS“.]

Better prediction leads to survival, adaptation, complexity

• More efficient survival.
  • Minimizing uncertainty through more accurate prediction.
  • Less energy wasted on inefficient individual perception and estimation.
• Faster adaptation.
  • Fine-tune the strategies and behavior of components.
  • Lower costs for components to explore a broader range of strategies.
• Increased complexity.
  • Freeing up of energy allows for more complexity.
    • [See also “The Vital Question“]
  • Emergence of hierarchical organization.
    • As components start using the consensus estimate, hierarchical organization emerges.
    • Slow variables that were derived from regularities occurring at a lower level become encoded in distinct properties at a higher level.
  • This happens when:
    • Components rely more on these (macroscopic) slow variables thans on (microscopic) local fluctuations.
    • Components estimates are largely in agreement (convergence on “good enough” estimates of underlying correlated behavior).
    • The value of the slow variable has functional consequences (at the system or component level).
    • The value of the slow variable is driven by component interactions in pursuit of a strategy.
    • Is sufficiently stable over a biologically relevant period of time.
  • Nested organizational levels.
    • In terms of space and time scales.
  • Each level is associated with a new, emergent function.
    • Consequences, pay-offs typical of that particular level.
    • For the system as a whole or its components.

Finding the “slow variables” that drive biological systems to better prediction.

• Need to understand the flow of continuous collective computation.
  • Collective estimation and compression of environmental regularities over time.
  • Inputs:
    • Multiple components interacting and implementing rules or strategies (microscopic behavior).
  • Algorithms:
    • Connecting the inputs and the outputs.
    • Mapping micro and macro.
    • Understanding the manner in which micro strategies combine to produce macro outputs.
  • Outputs:
    • Measured macroscopic behavior.
  • Flow:
    • Regularities in the environment -> summing of prior experiences -> predictions about strategy that improves fit -> behaviors.
• Which slow variables are biologically fundamental?
  • Derived from the microscopic level:
    • From data on interactions among components that are important to the system.
  • Feed back into the microscopic level:
    • The resulting slow variable needs to be read by and influence the behavior of components individually or collectively.
• Using strategically statistical mechanics to calculate emergent properties.
  • Discovering law-like behavior at the aggregate level.
  • Providing the microscopic basis for the macroscopic variables.
  • Exploring chains of probabilistic events that generate and respond efficiently to average features of the world.
    • Similar to physics: thermodynamics.
• Understand how underlying regularities are subjectively processed by the system.
  • Start at the bottom and work upward from the data: simulations.

Example: distribution of social power in animal groups.

• Slow variable: distribution of power.
  • Power = degree of consensus in the group that an individual can win fights.
  • Provides order, hierarchy.
  • Institutionalized: hard to change, slow time scale (many opinions need to change).
• Better prediction.
  • Power structure provides information about the future cost of interactions, the conflicts that can or can’t be afforded.
• Derived from micro-level interactions.
  • Bridging differing time scales.
  • Micro level: distribution of fighting ability (shorter time scale, individuals, changes faster).
  • Macro level: distribution of social power (longer time scale, collective, changes more slowly).
• Computation of slow variable (compression).
  • Summing up of the outcomes of many fights and conflicts over time encodes a slowly changing power structure.
  • Collective assessments of conflicts converge on a consensus about who has power.
• Slow variable leads to better prediction.
  • Power structure is a better predictor than the outcome of individual interactions.
  • Individual interactions can randomly / contextually fluctuate.
• Leading to increased complexity, hierarchy.
  • Fine tuning of strategies:
    • “Policing” becomes an affordable strategy.
    • Before the emergence of order, it was too costly a strategy.
    • With an established order, those high in status can “police” without being challenged.