The information contained in reputations is an incredibly valuable resource, which currently goes mostly unused. Reputations, and the specific way they are created and disseminated, can have a transformative impact on the collective behavior of the global population. By creating a machine-readable reputation system that collects data reliably and connects directly to our digital marketplace, we can use reputations to create a powerful new incentive system.

While we’ve already demonstrated how reputations can be monetized in principle, it is now time to show how this can be accomplished in practice.

First, we must create a reliable way to electronically evaluate goods and services, as evaluations are the raw material for machine-readable reputations. For a reputation to be usable, it has to correspond to reality as accurately as possible. The higher the correlation with reality, the easier it is for us to make decisions that lead to better outcomes.

Reality, as we know, is multifaceted and every object has multiple dimensions, which all have to be accounted for if we want the image we create to correspond with its underlying reality. This demand can have perilous consequences when we craft a usable reputation system: it is liable to become complicated, cumbersome and eventually maybe even unusable. As soon as we allow human language to become the primary driver of reviews and reputations, the process becomes so tedious that leaving reviews becomes a chore. Machine readability quickly goes out of the window, too.

So how should we proceed to avoid these pitfalls?

When we review goods and services traded in the marketplace, we could use three value axes of your choice to describe the good or service. The value axes combine any and every adjective and their opposite: for example, slow/fast, expensive/cheap, cold/hot. Each axis is graded on a sliding scale from −100 to +100 so that 0 represents the neutral middle. Minus 100 depicts the negative end (slow, expensive, cold) and +100 the positive end (fast, cheap, hot).

When we have used a good or a service, we can quickly review it by adjusting the most relevant sliders to positions that accurately convey our honest evaluations. A customer can move the sliders on the three axes by a combined 100 points. When you combine the information provided by multiple reviews, a complex and more complete picture of the object emerges as dozens of adjectives and their specific scales are used to describe it.

The purpose of these value axes is to allow organizations to compete in specific reputation categories of their choice. By doing so, they can specialize in a specific category the way a species will find their own ecological niche. Restaurants, for example, can compete with each other based on multiple reputation categories such as the quality of their service, atmosphere, cooking, price or how fast they can deliver. By specializing in certain reputation categories, each restaurant can cultivate their own specific customer base, allowing them to avoid head-to-head competition with restaurants that compete in other categories. This also allows customers to be better served.

Each service provider can choose the preferred three value axes they want their good or service to be evaluated on. This is how each actor can specialize in the reputation categories of their choice. However, a customer can choose whatever axes they find relevant and even initiate the creation of new axes altogether.

The review process is quick, and after a number of reviews, produces accurate depictions of the objects in question. The use of the value axes bypasses the need for language-based reviews, while still using a rich vocabulary to describe the objects. The sliding scales are also machine readable, which enables us to quickly access them in our electronic marketplace.

To understand how machine readability works in practice, let’s dig a little deeper.

First, the reputation data produced by every sliding scale review are recorded in the metadata of every electronic object (good or service) in our digital marketplace. Metadata are additional data embedded in electronic data, the way a photograph, which is a type of data in itself, can contain additional data of where and when the photo was taken. The reviews we provide thus become inseparable from the digital objects themselves.

Now, when we search for a particular good or service, we can access these stored reputations in the metadata to help us quickly narrow down what we are looking for. When we search the marketplace for what we need, we generally don’t just look for any generic product in that category, but something that fits our very specific requirements. This is where our custom-built search engine, the Value Search, becomes immensely helpful.

Value Search enables us to filter our searches further using the same reputation axes we used to evaluate goods and services. In addition to the keyword, we can narrow down our searches by using relevant sliders with the relevant adjectives such as expensive/cheap, far/near or ugly/beautiful. With the use of the sliders, we can dynamically weight certain search criteria over others in real time.

When we engage the value axes with our search engine, they act as filters that help us narrow down our searches to exactly what we are looking for. This filtration elevates search results with the exact qualities we are looking for to the top of the page in a fraction of a second. From thousands of possibilities in the marketplace, these sliding filters allow us to find the proverbial needle in the haystack. If we are looking for, say, a hotel room, we can find just the right option, say cheap, near and beautiful, out of thousands of room choices.

By bringing specific results to the top of the page, Value Search is a way to reward citizens and organizations for accumulating a particular reputation. A cell that has focused on being fast, cheap and reliable will stand out in these categories when these specific filters are applied. Value Search directs new customers to them and the increased demand for their goods or services allows them to charge higher prices.

In a system like this, it is easy to see the financial benefits good reputation brings. More customers and higher prices result in tangible financial rewards for upholding the values the customer is looking for. The payouts can be incremental, but much like a steady dividend, a good reputation can generate these payouts for a long time, possibly over the entire lifetime of the service provider. Cumulatively this can amount to a massive financial payout that can more than compensate for the additional costs and lost revenue that maintaining a good reputation usually entails.

Projects that don’t have as good a reputation will achieve a lower search result and the only way they can get their product near the top of the search page is by lowering their price. Here we can see how tangibly a bad reputation can erode pricing power, essentially incurring a financial penalty for ignoring the values and needs of their community. Exchanges that result from Value Search queries yield a type of instant financial karma, where citizens’ and organizations’ reputations are either rewarded or fined on the spot.

With the help of the Value Search search engine, machine-readable reputations can be dynamically monetized. Now building a good reputation can be as, or even more, profitable than simply trying to grab as much cash as you can, no matter the consequences. Reputation is thus a credible counter-weight to money and provides a distinct incentive of its own. This is good news because a good reputation corresponds to the values of the community, whereas focusing only on money is by and large a highly selfish proclivity.

In a world where nothing has value, but everything has a price, we have demonstrated how a well-built reputation system can balance the power money has had over our lives for so many years. Reputation is a powerful feedback loop and by systemizing how it is created and good reputation is rewarded, we can use it to create a more just and fair society. The experience of fairness creates trust, good will and hope for the future. These sentiments are immensely beneficial to our society and the planet as a whole.