Ambient Attribution

Ambient Attribution arises from the interaction of Contribution, Attribution and Transparency, and depends heavily on Reputation.

It describes permissionless Contribution by any User, in whatever form, that results in an Output or Event recorded on the DCM. The Contribution receives fair recompense (Attribution) based on the value it provided whenever that work is used or reused and benefits from a Dispersal Event. Being permissionless, Ambient Attribution is most powerful when it builds on prior Contributions from other Users.

Ambient Attribution ultimately aims to resist corruption, through fairness, Radical transparency , attack mitigation (eg: lawsuits and corporation pressures), dishonesty, disputes, transaction costs, and management overheads. Although such a perfected system may be some way off, the core DPL and DCM is a first attempt, and will no-doubt be improved over time.

The basic components implemented in this first iteration are:

1. Transparency of operation - all Contributions must be open to scrutiny.
2. Availability to be built upon - all prior-art Contributions must be available for further innovation.
3. Fairness - all in the network should gain from their Contributions in proportion to their utility.
4. Leverage of Reputation - recognising that in a permissionless system that are likely to be many ways to game for benefit, User actions are immutably recorded and publically available, and can be used by future Contributors to judge whether or not to engage.

Ambient Attribution does this by recording all Contributions as Traces, and exposing those to market forces to say:

1. Which have been used,
2. Which can be re-used (and therefore reduce duplication of effort),
3. Which can prove and deliver to the market (and therefore avoid pointless projects)
4. Which effectively deployed capital.

The market forces of Ambient Attribution are:

1. Innate integrity in actors - self seeking fairness
2. Public projection of integrity (or lack thereof) in actors - being seen to do the right thing; being seen not to do the right thing.
3. Security - purchasing Traces secure versioning of software
4. Fear of missing out - early adopter payments are considered Contributions, and subject to Attribution later in the project lifecycle

It does so through creating the last social network - a network for business innovation.

Future Improvement

The aims of Ambient Attribution, and the current invocation of it, can be considered against these potential benefits:

An open source funding model that rewards contribution

The current open source attribution model doesn't incorporate financial remuneration of Contribution. Neither does it give a practical method of operating a proven business model. The result is that contributors (devs) are under incentivised, and organisations must shoe-horn in revenue models by e.g. advertising, token sales, partial closed source, or support.

Ambient Attribution can therefore be judged on up-take within the open source community as a better way to fund and be paid, while maintaining the ethos of open-source.

Reduces transaction costs and market inefficiencies

Ambient attribution is expected to:

1. Reduce market inefficiencies through better delineation of property rights including fractional rights of Traces.
2. Reduce the friction of property rights by benefitting open source, attributable work.
3. Reduce Deadweight Loss work, i.e. Middlemen, including:
   1. Recruitment agencies
   2. Brokers of capital deployment
   3. Distributors of pre-existing software without value-add services
   4. Service pass-through agencies

Enables permissionless innovation

Innovation is currently closed-source largely, and owned by a small number of large companies, using a model of recruitment, centralised innovation and IP rights.

That leaves a great deal of talent untapped.

Dreamcatcher is a permissionless platform to untap that potential and aims to reduce the barriers to entry to be least possible to carry out online .

Transparency

Inefficiency and unfairness tends to arise from a high information gradient - money is to be made by restricting access to that innovation, restricting the knowledge used to produce it and restricting a detailed knowledge of the problem that was being solved. Although information gradients benefit the few, it tends to end up in a zero sum game (e.g. when a software house knows how to build software, but not in detail business problem to be solved; the business knows the problem, but not how to build software; and the resulting app is kept closed.)

This does not optimise innovation.

By encouraging transparency, Dreamcatcher incentivises all involved to work in the open, with Ambient Attribution allaying any fears of being ripped off.

Optional anonymity

While encouraging transparency, in hand with that we allow optional anonymity.

We aim to allow the greatest amount of sharing of all activity, but some may feel that an intrusion when earthed to a real-world identity. Therefore we allow any entity to remain anonymous. However, all Users have an indelible Reputation to consider, which is not optional.

Reputation Management

Although Access Control can limit transparency for code, all activity on the network is transparent. That is, the fact of a User’s behaviour as specified by an Arbiter is open to all, as is the fact of well-concluded Contracts; likewise the lack of any such interaction is also known, and can be accounted for by any User wanting to interact with them in the future. And so using optional anonymity in ways that do not benefit the network is managed by permanent reputation recording.

Principles of Ambient Attribution are:

1. Highest granularity of data is used
2. Dispersal is by algorithm acting upon data relating to the things being paid for
3. Dispersal should always happen at time of payment
4. Dispersal should be predictable ahead of time
5. People selecting the algorithm for a project should be disinterested in that project