Transformation is the set of actions between a source state and a target state.
Transformation as a graph
If a state is a node and an action is an edge, a transformation can be modeled as a path:
node → edge → node → edge → node
In this model, a transformation is a subset of a graph. A single action moves the system to a neighboring state, while a composition of actions traces a longer path through the possibility space.
A problem is a chosen transformation. To solve it is to arrive at the target state. Not every reachable state is desirable, so deciding which transformation to pursue matters as much as performing it.
Parameters, change, and identity
Every transformation changes some parameters, but usually not all of them. Anything we do unfolds through time even when its other measurable properties remain unchanged.
An identity transformation preserves the property we care about:
f: A → A
This does not mean that nothing happened. A living system, for example, performs continuous transformations to preserve itself. What we call its identity is the set of invariants maintained through those changes.
Transformations also compose. What appears to be one action at a higher level is often a combination of smaller transformations underneath it.
Computation and reducibility
A computation takes an input and systematically transforms it into an output by applying rules. Programming is the work of designing those rules.
A formula is a form of computational reducibility: it compresses many symbolic steps into a shortcut. But not every process has such a shortcut. In a computationally irreducible system, the only way to know the result is to run the process step by step.
This is why the passage of time is meaningful. If every process could jump directly to its final state, there would be no need to live through the transformations in between.
Productivity as action management
Productivity is the degree of useful transformation toward a target state. It is less about managing time—which we cannot control—and more about managing the actions we take while time passes.
To accelerate a transformation:
- Avoid blunders: actions that do not align with the target or silently move us away from it.
- Get the basics right for the current condition: exercise, sleep, eat real food, and embrace simplicity.
- Define the problem precisely: the current state, target state, inputs, outputs, and constraints.
- Choose the best available strategy and tools.
- Prefer concise explanations and clarity of thought; both reduce unnecessary transformations.
- Experiment, observe feedback, and adapt.
There are always transformations available. When the next useful action is unclear, resting or exploring can expand the graph until a path becomes visible.
Value and delegated transformation
Value is information about a delegated transformation: someone else can perform an action that moves us toward a state we want. Money stores and transfers that value.
Design is also a statement about transformation. It defines how something works by selecting a preferred set of actions and outcomes from all the possibilities.
An idea, meanwhile, emerges from connections among entities already held in memory. Research expands that graph by discovering new entities and relations, creating new paths and sometimes new shortcuts.
Life as a game of transformation
A game can be described with four parts:
- Current state
- Constraints
- Possible moves
- Target state
The constraints define the possible moves. Applying those moves to the current state produces a transformation toward—or away from—the target.
This makes incentives important. Before optimizing effort, verify that the game rewards what you are trying to improve. Do not work harder inside a system that rewards the wrong thing. Understand its incentives, change the game when possible, or choose a different one.
Life is constant work on chosen, desirable transformations. The question is not only how to move faster, but which target states are worth moving toward.