Economies of Trust
The recent string catastrophes that landed on several of Airbnb’s users has highlighted a complex challenge in the service’s role as a broker among total strangers. How do you know you can trust that person on the other end of the transaction? And if something happens, who has your back? Until now, that question was left to get sorted out for itself.
The heart of this issue isn’t particularly new. Trust is a function of reliability among actors within a given context, and one of our top projects in life is to continuously sort this out. The difference is that Airbnb’s users have neither the years of history that you have within your own networks of friends, family, and colleagues, nor the consequences of violating that trust once it has been formed. Who would you be more willing to open your home to: a total stranger from another corner of the map, or your best friend from college? Easy answer.
On the other side of the transaction, however, Airbnb offers affordable alternatives to hotels and hostels, with the added potential of a warm welcome from your hosts, along with friendly conversation and a local’s perspective on the place you’re visiting. I believe this is (and always was) possible just by leveraging the full scope of the many networks you already belong to.
One degree: your friends
Consider your immediate network of contacts — the people you interact with on a regular basis. Chances are, if you were to plot these contacts against a map, you’re not going find many places that you haven’t already visited. In my case, this network plot reads like a map of places I’ve lived long enough to form solid friendships, or places my fellow classmates have recently moved to since graduation. There are a few new places that I would like to visit, but only a few.
Next, consider the networks that these friends belong to: your friends’ friends.
Two degrees: your friends’ friends
As a general rule of network analysis, all of your friends have more friends than you do. They’re also slightly more central in the crowd than you are. This is the 2nd degree of your network, and it dots the globe, reaching to far away continents and clustering around the immediate vicinity of your friends, just as they are clustered around you.
Now let’s jump out just one more degree:
Three degrees: friends of your friends’ friends
These illustrations really don’t capture the true density of the network that surrounds you at greater degrees of association. The leap in connections is massive. Imagine if you could visualize the 2nd and 3rd degree geography of your network, and trace the relationships back to your immediate friends and relatives responsible for that connection. Now imagine you want to spend Christmas in Prague. There’s a great possibility that you are one or two polite introductions away from making that happen.
Granted, this model still lends itself to the potential for manipulation. For better or for worse, you just can’t design human nature out of the system. However, what this model does have is one of the most sophisticated filters ever devised: trust. Social clusters are inherently self-validating, and unsavory characters are typically denied access to our most important connections. The consequences for violating trust across your close friends’ networks would be far more severe than doing the same to a perfect stranger.
The challenge to pulling something like this off is to develop a critical mass of participants within a traceable network, so that multiple degrees can be calculated. With over 750 million members, Facebook is the perfect network to make such a service possible. Many network graphing apps already exist, so it doesn’t seem like an unreasonable technological feat.
So what do you think? As either a traveler or a host, does a service model based on this premise sound appealing?
