Fairness History
Overview
Most electricity markets treat every market interval independently.
Once an auction has cleared, the market forgets what happened and begins again with no memory of previous outcomes.
FP-AMM takes a different approach.
Fair Play maintains a fairness history for every participant, allowing previous allocation decisions to influence future ones.
This transforms fairness from a single-period optimisation problem into a continuous property of the market.
Rather than asking whether today's allocation is fair, FP-AMM asks a more meaningful question:
Has each participant been treated fairly over time?
Why Memory Matters
Periods of scarcity and surplus are inevitable in modern electricity systems.
What matters is not that an individual participant experiences a single interruption or curtailment, but whether that burden is shared fairly across repeated events.
Without memory, the same participant could repeatedly be selected simply because they happen to occupy the same position in a queue or are evaluated first by a deterministic algorithm.
Over weeks, months or years, this creates persistent disadvantage despite every individual market interval appearing locally reasonable.
Fairness History prevents this outcome.
A Stateful Market
Unlike traditional electricity markets, FP-AMM is stateful.
Each participant accumulates a record of previous allocation outcomes.
For consumers, this reflects how consistently requested services have been delivered during periods of scarcity.
For generators, it reflects how consistently available generation has been accepted during periods of surplus.
This historical information forms part of the market state and evolves continuously alongside prices, commitments and network conditions.
The market therefore remembers previous outcomes when making future allocation decisions.
Fairness as Negative Feedback
Fairness History introduces a negative feedback mechanism into the allocation process.
Participants that have historically experienced less favourable outcomes gradually receive greater priority during future allocation decisions.
Conversely, participants that have consistently benefited become marginally more likely to contribute when further flexibility is required.
Rather than allowing inequality to accumulate, the market continuously works to reduce it.
This self-correcting behaviour allows fairness to emerge naturally over repeated market interactions.
Fairness is Longitudinal
Fairness should not be judged by a single market interval.
One participant may occasionally experience a delayed charging session or a curtailed export.
That alone does not imply unfairness.
Instead, fairness is evaluated across the complete sequence of market interactions.
If participants contribute similar levels of flexibility over time, they should experience broadly similar outcomes over time.
Fairness History therefore measures cumulative experience rather than instantaneous outcomes.
Independent of Market Size
Fairness History naturally adapts as participants join or leave the market.
New participants begin with a neutral fairness state.
Existing participants continue to accumulate their historical record.
Because fairness is calculated individually rather than through fixed queue positions, the mechanism remains effective regardless of the number of participants connected to the system.
Whether coordinating hundreds of households or millions of distributed energy resources, the same principles continue to apply.
Supporting Service Levels
Fairness History does not replace contractual commitments.
Service Levels continue to determine the level of reliability each participant has chosen.
Instead, Fairness History operates within those contractual boundaries.
For example, two participants with the same Service Level may both be eligible for temporary deferral during scarcity.
Fairness History helps determine which participant should contribute on this occasion by considering their previous allocation experience.
Contractual guarantees therefore remain intact while fairness is continuously balanced over time.
Building Trust
Participants are more likely to provide flexibility when they believe the market behaves predictably and impartially.
A market that repeatedly selects the same households, businesses or generators quickly loses credibility.
By maintaining a transparent fairness history, FP-AMM allows participants to understand that temporary reductions in service are not arbitrary, but form part of a system that continuously balances outcomes across all participants.
This increases confidence that flexibility is being shared rather than imposed.
Relationship to the Allocation Algorithm
Fairness History records previous allocation outcomes.
The Allocation Algorithm determines how that historical information influences future decisions.
Together they form a closed feedback loop.
- Market outcomes update each participant's fairness history.
- Fairness history influences future allocation probabilities.
- New allocation decisions create updated fairness histories.
Over time, this continual feedback drives the market towards increasingly balanced outcomes while respecting Service Levels, physical feasibility and economic efficiency.
Next
The next section describes the Allocation Algorithm, showing how Fair Play uses Service Levels, fairness history and probabilistic selection to allocate scarce resources during periods of scarcity and surplus.