By: David Krakauer, Nils Bertschinger, Eckehard Olbrich, Jessica C. Flack, Nihat Ay
Date: 24 March, 2020
- There is little agreement (in biology) about what individuals are.
- Few rigorous quantitative methods for their identification.
- Information theory approach to perceive and define individuals:
- Individuals are aggregates that preserve a measure of temporal integrity.
- They “propagate” information from their past into their futures.
- Systems that are sufficient predictors of their own future.
- Derivation of three principled and distinct forms of individuality:
- An organismal, a colonial, and a driven form.
- Depending on degree of environmental dependence and inherited information.
- This approach allows you to expand the scope of individuals …
- Multi-scale, highly distributed, and without physical boundaries.
- … and investigate how adaptive (learning) systems emerge during evolution.
- Organizing principle is maximizing the reduction of environmental uncertainty.
- Nested structures emerge that compress information into “slower” variables that lead to better predictions (“coarse-graining”).
Biology and the need to define individuality
- From the perspective of physics and chemistry, biological life is surprising.
- Physics and chemistry are universal.
- Biology may exclusively be a property of Earth.
- There is an asymmetry between the certainty of what you learn from working down into a system’s components (Reduction) …
- When you break down a system into ever more elementary constituents, nothing is left unaccounted for.
- As in: break down a biological system, there is no unexplained chemistry or physics left.
- … versus the uncertainty of working up through aggregation (Emergence).
- Difficult to predict properties of aggregates from knowledge of constituents.
- As in: no physical or chemical theory can predict biology.
- So, when you want to explore biological ideas, you can’t start from the first principles of physics and chemistry.
- Biological science requires picking a level or unit of analysis (individual).
- To be able to talk about metabolism, behavior, the genome.
- Human perceptual bias for certain kinds of analysis and aggregation.
- But what constitutes an individual?
- How far up or down do you go?
- What principles do you use to explain aggregate properties?
- Ideal case:
- Perceiving and defining an individual relies on minimal prior knowledge.
Previous approaches to define individuality
- Principles of replication and shared genetic ancestry.
- Individuals increase in relative frequency by exploiting a source of metabolic free energy.
- Individuals respond adaptively to environment.
- Individuals have tightly coordinated relationships (chemical, physiological, computational) among their parts.
- Principles of members (individuals) and complement (environment).
- Separation of self and non-self.
- Individual as a temporal aggregate encoding a common past separable from the past of other aggregates.
- Individual as a spatially bounded collection of metabolic reactions insulated by a membrane from reactions in the environment.
- Individual as the unit of selection and evolutionary change.
- These approaches struggle to explain individuality at multiple organizational levels.
- Ants versus ant colonies (only some ants reproduce, the colony as a whole adapts).
- Viruses (can replicate, adapt, have a persistent identity, but rely on environment for replication).
A different approach: how to separate the figure and the background.
- The background of an image carries as much, if not more, information as the object.
- The challenge is to separate the two.
- Rather than assume that they are already distinct and independent.
- Individuality can be continuous.
- Some processes possess greater individuality than others.
- Individuality can emerge at any level of organization.
- Find fundamental, rather that derivative, properties of individuality.
- Individuality can be nested.
- Individuals are information hierarchies.
- Its components estimate regularities in the (fast-moving) environment.
- Hierarchy of components compresses time series data into “slower” variables.
- If slower variables better predict future than fast underlying components, new levels of organization can emerge.
- Use perceived regularities to tune collective strategies.
- So, individuals are best thought of in terms of dynamical processes.
- Not as stationary objects that leave information-theoretic traces.
Information theory approach
- Are aggregates that propagate information from the past to the future.
- Have temporal integrity.
- Meaning: uncertainty is reduced over time.
- Initial framework: measures the energy lost from the total available energy available for performing work (Clausius, 1860s).
- Then: measures the potential disorder in a system (number of unobservable microstates consistent with observed macrostate) (Boltzmann, 1877).
- Shannon entropy in information theory:
- Maximum number of states that can be transmitted from one point to another across a channel, in the face of noise.
- A target word will be disordered during transmission in proportion to the noise in a channel.
- Information is minimized when predictability is maximized.
- High entropy = high information = many possible states = minimal predictability.
- Low entropy = low information = outcome is known = maximal predictability.
- [See “The User Illusion”: As the amount of entropy/disorder increases, more information is needed to describe a system (for instance, a sequence of 100 random numbers is more difficult to describe than a sequence of 100 zeroes). Information is directly linked with entropy, is similarly a measure of randomness/disorder and can be described as a measure of how surprised we are (there is more surprise in randomness than in order).].
- Wide application:
- Phone calls: increased entropy = less light pulses = bad reception.
- DNA: increased entropy = mutations = altered phenotype.
- If the information transmitted forward in time is close to maximal, evidence for individuality.
- Defining properties:
- Partitioning states into the system and its environment.
- How does the current state (system or environment) determine the future state.
- To what degree does the history of the system and/or the environment drive the future.
- Three quantities corresponding to a type of individuality:
- Quantified by measures in terms of:
- Shared information: shared by system and environment (e.g., adaptive information).
- Unique information: unique to either the system or the environment (e.g., memory in each).
- Regulatory information: depends in some complicated way on both the system and the environment (e.g., regulatory information).
- Organismal individuality maximizes.
- Maximizes shared information and unique information of the system.
- Need a large amount of private information required for effective function.
- Adaptation through shared information about the environment in which they live.
- Colonial individuality.
- Maximizes regulatory information and unique information of the environment.
- Environmentally regulated aggregations.
- Share only a small amount of information with the environment.
- Adaptation through regulatory mechanisms and interaction with the environment.
- Degree of environmental determination.
- When minimized, individual is not influenced by the environment and doesn’t adapt.
- Unique environmental memory can be maintained by interacting with the system.
- Environmental information can be encoded by inheritance of shared information (nature) or ongoing regulatory information interaction (nurture).
How do adaptive systems emerge?
- Organizing principle of adaptive systems: maximizing reduction in environmental uncertainty.
- Regular environment (patterns).
- Construction of a nested process (hierarchy).
- Compression of fast, microscopic dynamics into slow variables (computation).
- Slow variables become better predictors of the future than underlying fast movements of components (learning, prediction).
- [Application of collective strategy.]
- [Random variation in replication?]
- [This perhaps explains the first stage of self-organization: why do connections form among parts to form a system – see “At Home in the Universe“. From there, systems (and order) potentially grows, as they self-organize and become sufficiently robust to replicate reliably and adapt when needed.]