Design Principles (Deep Dive)
How human-centered systems behave in the real world
The Human-Centered Systems Manifesto defines a small set of design principles. This page explains how those principles translate into real decisions, trade-offs, and structures across economics, technology, and institutions.
These are not ideals. They are design constraints.
1. Dignity Before Efficiency
What this principle means
Efficiency measures speed, cost, and output. Dignity measures whether people retain agency, stability, and meaningful participation.
A system that maximizes efficiency without regard for dignity eventually treats humans as expendable.
What this looks like in practice
In economic systems
- Workers are not treated purely as variable costs
- Income volatility is reduced, not normalized
- People are partners or participants, not disposable inputs
Example
In BangNano-style community economics, financing is structured around shared risk and asset ownership rather than interest-bearing debt. This slows down capital turnover, but it prevents people from being trapped by compounding obligations during downturns.
Efficiency is traded for stability and dignity.
In technology systems
- Automation supports human decision-making instead of replacing it
- People can intervene, override, or understand outcomes
Example
RoboHen workflows are designed with explicit human-in-the-loop checkpoints. This may reduce raw throughput, but it preserves accountability and prevents silent harm caused by fully automated decisions.
2. Incentives Over Intentions
What this principle means
Systems do not respond to values statements. They respond to incentives.
If unethical behavior is rewarded financially, socially, or operationally, it will dominate regardless of stated intent.
What this looks like in practice
In institutions
- Performance metrics are aligned with long-term outcomes
- Short-term optimization is constrained
Example
In debt-driven financial systems, lenders profit when borrowers remain dependent. In asset-based models, value is created only when productive activity succeeds. The incentive structure changes behavior without requiring moral persuasion.
In AI systems
- Models are evaluated not just on accuracy, but on downstream effects
- Deployment incentives discourage silent harm
Example
HAIAL governance principles require AI systems to be evaluated on whether they increase human economic participation. A system that improves efficiency but reduces livelihoods fails the incentive test, regardless of performance metrics.
3. Asset-Based, Not Debt-Driven
What this principle means
Debt shifts risk downward and concentrates control upward. Assets distribute control and align incentives over time.
Debt-based systems appear efficient in the short term but are fragile under stress.
What this looks like in practice
In personal and community economics
- Ownership is prioritized over leverage
- Growth is tied to productive capacity
Example
Instead of financing consumption through credit, BangNano-style models focus on building or acquiring productive assets collaboratively. Returns come from real activity, not financial engineering.
This slows growth but increases resilience.
In institutional design
- Balance sheets reflect real assets rather than abstract claims
- Financial expansion is constrained by productive reality
Example
FAIR Economy principles require full-reserve, asset-backed structures. This removes the ability to create money through debt expansion, reducing systemic fragility and speculative bubbles.
4. Transparency Creates Trust
What this principle means
Trust cannot be demanded or branded. It emerges when rules, flows, and decisions are visible and understandable.
Opacity concentrates power and externalizes risk.
What this looks like in practice
In economic systems
- Participants can see how value flows
- Rules are explicit and stable
Example
BangNano transaction systems emphasize clarity over complexity. Members understand how funds are used, how returns are generated, and how decisions are made. This reduces dependency on authority figures.
In technology systems
- Automated decisions are explainable
- Logs and audit trails are accessible
Example
RoboHen workflows are structured so execution paths and decision points are visible. This allows organizations to trace outcomes, correct errors, and assign responsibility without ambiguity.
5. Humans Remain Central
What this principle means
Technology should expand what humans can do. It should not remove people from economic life by default.
Displacement is not inevitable. It is a design choice.
What this looks like in practice
In AI deployment
- Systems are designed to augment roles rather than eliminate them
- Human judgment is preserved where stakes are high
Example
HAIAL promotes AI systems that increase the productivity and reach of workers, such as enabling small teams to operate at higher capacity, rather than replacing them entirely.
In automation
- Humans remain accountable for outcomes
- Automation serves intent, not the other way around
Example
RoboHen separates data flow from decision authority. Automation executes tasks, but intent and approval remain human-controlled, preventing systems from drifting away from human goals.
6. Trade-Offs Are Explicit, Not Hidden
A defining feature of human-centered systems is that trade-offs are acknowledged rather than obscured.
Slower growth may be accepted to gain resilience.
Lower efficiency may be accepted to preserve dignity.
Reduced scale may be accepted to maintain accountability.
Hidden trade-offs produce fragile systems. Explicit trade-offs produce durable ones.
7. A Shared Pattern
Across economics, technology, and institutions, the same pattern appears:
- Systems designed for short-term efficiency externalize long-term harm
- Systems designed with ethical constraints endure stress better
- Systems that keep humans central adapt more effectively over time
These principles do not guarantee success. They increase the probability of resilience.
Closing
Design principles only matter when they constrain real decisions.
This page exists to make those constraints visible and testable, so builders can apply them deliberately rather than intuitively.
The goal is not perfection.
It is alignment between values, incentives, and outcomes.