This scenario should explain how someone could use a barter banking system extensively in a world where the barter banking system was used extensively to relocate for a job.
Adam is a regular, single guy, an accountant with a house, car, job, etc., living in Baltimore.
His employer - an accounting firm that specializes dealing with high-end clients who use the barter banking system extensively - wants him to move to Miami. This would involve 'selling' his house and moving all of his stuff to Miami.
Now, instead of contracting a real-estate agent, moving company, etc., everything is taken care of by the barter banking system on his mobile phone, all within the span of a week.
Day1
Adam deposits nearly all of his stuff into the barter banking system - house, car, sofa, bedroom set, appliances, even his cutterly. All that he doesn't deposit are the clothes on his back, a pair of shoes, his wallet with his id and various cards, his phone, and a duffel-bag with some personal effects for which he holds some sentimental attachment.
He walks out his front door, hails a cab, and starts off to the airport. On the way to the airport, the barter banking system has already matched through the various barter chains (that in this world form a very complicated network), a flight to and hotel stay in Miami.
So far, this hasn't cost him anything, because the company is paying for his move and putting him up in a hotel for a maximum of 2 weeks, with an expected cost to the company of 2 office desks, 12 hours service time to a client, and $2,500. The cab ride is paid for in cash with his company's credit card. Keep in mind that the extensive use of barter banking system does NOT replace the monetary system, for there are RARE cases where the use of the monetary system is deemed to be more efficient.
Back to the scenario.
Adam has opted to allow the the barter banking system virtually complete autonomy in authorizing transactions to facilitate his move, and as such the system goes about trying to find the most 'profitable' deal for Adam's home and stuff. This involves finding a 'buyer' of Adam's home who would also 'buy' a lot of Adam's stuff, so that there would be less stuff to have to be relocated and stored in a storage facility pending its 'sale' to other 'buyers'. Therefore, out of all of the potential 'buyers' that the system identifies, it determines which of those potential 'buyers' would also 'buy' a lot of Adam's stuff, and do so EITHER by barter or with money, and which buyer would barter and/or purchase with money the stuff at the best 'price' or best 'relative value'. The system sends alerts to those 'buyers' with offers that they have the choice to accept or decline. These alerts show up to those potential home buyers in the form of a list of items that are included with the house. Only the home buyers are included in these alerts at this point, and only those home buyers who are in a position to move into the house quickly. The system won't present the same offer to all buyers mind you but better offers to those home-buyers who will accept more of Adam's stuff and move in more quickly than others.
Now, as it happens, there is a guy whose name is Bill, who is enroute to Baltimore from Miami. He is on a plane, scheduled to arrive at 3pm - 2 hours from now. He is also 'selling' his home in Miami, along with a lot of his stuff.
The system thus recognizes these two clients as potential matches for a good 'match' for the exchange of each others' homes and stuff in a way that would be highly profitable to both parties. Bill has also opted to allow the barter banking system near full autonomy in conducting transactions on his behalf, within certain limits.
So, within mere minutes, before Adam even arrives at the corner down the street from his home, he is presented with an offer for his home, and Bill for his home, along with respective list of items both Adam and Bill can trade. Both Adam and Bill consider the offers of the respective homes and items. Both contemplating what they would be willing to accept of each others' items, the system providing recommended trades.
Keep in mind that the system is also presenting both Adam and Bill other potential matches in real-time. The system has determined that Bill and Adam are most probably the best of the other highly probable 'matches' out there at this time and that the odds of finding a better potential match are such that there probably won't be a better match for about 14 days. Keep in mind that all potential matches are also currently being alerted of the various probability of finding better matches as well.
Nevertheless, neither Adam nor Bill trust the system completely. As such, Bill has perused multiple offers since leaving Baltimore. Adam's home and items interest him. He tends to agree that Adam's 'probable offer' that the system calculates would probably be the best one he's seen so far. Likewise, Adam, thinks the offer is good, but would nevertheless like to wait a while longer and see what else the system might come up with.
Over the course of the afternoon, Adam peruses other offers while waiting for his flight. Bill has landed in Baltimore and is on his way to a hotel. Adam finally settles into his airplane seat and has himself a drink. Then, the system alerts Adam that Bill has opted to present a verified offer. The system thinks is the best and recommends that Adam submits a verified offer for Bill's home and items.
Adam peruses home's and items' specs as well as the data of the cost analysis the system forecasts he will experience if he waits longer. It shows him that essentially he could expect a better match only after 14 days (with a margin of error of 11.6%). He takes this to mean that, essentially, for the next 14 days Bill's offer is probably the best he is going to get.
The system has a good track record of being accurate in this regard, and so before Bill's verified offer expires, he opts to present Bill a counter offer. Adam thinks that since the company will put him up in a hotel for 2 weeks anyway, he might as well try and low-ball Bill.
Bill gets the verified counter offer instantly as Adam inputs it, along with the option to look at the same sort of data and forecasts that indicate when he will most likely get a better offer. Bill counters in real-time accepting or declining Adam's items accordingly. Adam likewise provides a verified counter-offer in real-time as he accepts or declines Bill's items accordingly, all as forecasts of when each will probably receive better offers are updated in real-time as well. Their respective deals involved exchanging not only the homes but also at various stages in the bargaining process somewhere between 60% and 90% of each others' items.
At last, though there's no deal. Bill realizes that Adam is trying to low-ball him and rescinds his offer. Adam doesn't care, again, since he has 2 weeks in a hotel paid by the company. He turns off his phone and enjoys another drink.
Bill isn't too perturbed, as this is essentially how it goes. He puts his phone down and watches a movie. His company is also putting him up in a hotel for a couple weeks.
Eventually, both parties make deals with others, deals which were not as good as this first deal.
A week later Bill ends up trading his house to someone who accepted 80% of his stuff.
Six days later, Adam, however, made a deal with someone who accepted only 20% of his stuff.
Both Bill and Adam move into their new homes a week later. How their respective deals work out in detail will follow in another post.
Day 2 to follow.