To provide a balanced, two-sided analysis of measurement in policy formation, we must examine how systems thinking and complexity science evaluate both the strengths and weaknesses of absolute and relative measurements.
Absolute Measurements in Policy Formation
The Merits of Absolute Measurements
- Evaluating Tangible System Viability: In methodologies like the Theory of Constraints (TOC), absolute measurements are essential for objectively tracking the literal survival and success of a system. TOC relies on absolute, concrete financial metrics—Throughput (actual money generated), Inventory (money invested), and Operating Expense (money spent)—to cut through organizational noise and determine if a system is actually achieving its goal,.
- Preventing Statistical Manipulation and “Spin”: Absolute measurements are necessary to ground data in reality and prevent the exaggeration of risk. When evaluating statistical claims for policy, David Spiegelhalter advises asking, “Is the claimed effect actually practically important?” and “Is the story being spun?”,. (Note: As established in our previous conversation, information outside these specific source texts details that Spiegelhalter advocates for absolute risk metrics to prevent the panic caused by relative risk metrics—e.g., a “100% relative risk increase” might only represent an absolute jump from 1 in 10,000 to 2 in 10,000. You may want to independently verify his broader work on this topic).
- Optimization in “Hard” Systems: When a policy addresses a “Type 1” or “Type 2” situation—where objectives are clearly defined, uncontested, and highly quantifiable—absolute measurements allow for “Hard Systems” engineering. This enables policymakers to mathematically model and optimize the single most efficient route to a specific goal,,.
The Issues with Absolute Measurements
- The Illusion of Objectivity (Technopoly): Neil Postman warns that relying heavily on absolute metrics (like standardized test scores or polling numbers) creates a “magical illusion of exactitude and objectivity”,. In a “Technopoly,” this reduces complex human citizens into “calculable persons” and strips away the rich, ambiguous context required for sane policymaking,.
- Systemic Fragility and Failure Demand: John Seddon argues that when management establishes arbitrary, absolute numerical targets, it forces workers to sub-optimize the system just to hit the number,. This destroys the economy of flow and generates massive “failure demand” (waste caused by failing to serve the actual needs of the citizen),.
- Hiding Ethical Boundary Judgments: Critical Systems thinkers like C. West Churchman warn that an absolute “measure of performance” is never an objective fact of nature; it is a subjective boundary judgment,. Optimizing for an absolute metric (like maximum economic output) inherently decides who acts as the beneficiary and who is marginalized, often hiding the ethical cost shifted to the wider environment,.
Relative Measurements in Policy Formation
The Merits of Relative Measurements
- Matching Environmental Complexity: W. Ross Ashby’s Law of Requisite Variety proves that a policy’s effectiveness must be measured relatively. To survive and maintain control, the internal variety (number of possible responses) of a management system must be equal to or greater than the variety of the environmental disturbances it faces,. An absolute count of resources is meaningless if it does not relatively match the complexity of the environment.
- Navigating Relationship-Maintenance: Geoffrey Vickers notes that complex human systems do not seek absolute end-states; they are “relationship-maintaining” entities,. Policy should therefore be guided by relative “mismatch signals”—the friction generated when the perceived reality (“what is”) deviates from the society’s evolving, tacit standards (“what ought to be”),.
- Practical Uncertainty Reduction: Douglas Hubbard argues that measurement does not require absolute certainty. Instead, measurement is valuable if it relatively reduces a decision-maker’s current state of ignorance,. By calculating the relative “Value of Information,” policymakers can measure only what directly reduces the risk of a decision, rather than wasting resources seeking absolute precision,.
The Issues with Relative Measurements
- The Danger of “Spreadthink” and Paralysis: Because relative measurements depend on the observer’s subjective perspective (“objectivity-in-parenthesis”), different stakeholders will evaluate the exact same policy completely differently based on their worldview,. John Warfield identifies this as “Spreadthink”—the reality that individuals’ views on the relative importance of problems will be spread all over the map, with almost zero natural consensus,. Without formal structuring tools, relative measurement leads to endless debate and policy paralysis,.
- The Risk of Extreme Relativism: Paul Cilliers warns that acknowledging the relative, observer-dependent nature of complex systems can dangerously slide into a relativism where “anything goes”. If policy is entirely relative, it becomes difficult to establish binding ethical constraints, necessitating an “agonistic” network of debate to ensure diverse voices don’t result in total loss of systemic cohesion,.
- Misleading Narratives and Spurious Causation: As complexity science notes, in highly interconnected systems, measuring relative correlations can be deceiving. If policy is based merely on relative associations rather than mapping absolute, structural cause-and-effect (like the Logical Thinking Process demands), decision-makers risk treating symptoms rather than fundamental constraints,.