Computers and humans think in a fundamentally different way when it comes to the concept of "the truth". A computer's understanding of the truth is based on if statements: decisions that give a binary outcome based on a piece of supplied data. To work out if I can take money out of an ATM, somewhere in the depths of the ATM's code base will be something along the lines of:
If the amount in this person's account is greater than amount being withdrawn, allow the withdrawal
If I wasn’t telling the truth - attempting to withdraw more money than was in my account, the ATM can easily tell because it has access to a data set of truth (my account balance) that it can easily compare with the data I supply (my request for cash). The truth here is always true and impossible to manipulate (without directly hacking the bank): a computer cannot be fooled when given the correct data.
When it comes to humans, there is a much more nuanced and discretionary understanding of the truth. As the magician and psychologist Derren Brown has demonstrated, even a basic question like “can I buy this item” can be manipulated based on emotional and subconscious cues. This allowed him to walk into a jewellery shop in the US and buy a necklace with blank paper instead of bank notes.
He has given the right data (which would have failed the ATM’s IF test), but the way that he supplied the data (with a healthy dose of distraction and manipulation) caused the shopkeeper to make an incorrect judgement about what was true and what was false.
Even with one of the most supposedly objective systems: the law, there are many more nuances than we think. In fact, the entire judicial system is, in many ways, a result of the necessity for discretion. Killing is wrong, but under certain circumstances and with certain mitigations it becomes right. There is rarely a binary answer as there is in computing and so the translation of these nuances into absolutes becomes a challenge for a computer scientist.
This becomes problematic when one considers the implications of this “fuzzy thinking” for a technology that gives the impression of authority: blockchain. By having an “immutable” ledger many proponents of blockchain suggest that the issue of lying can be reduced through a combination of an incorruptible audit trail (you can see all transactions that have ever been entered into the blockchain) and the decentralised nature of the transaction authentication (you need 50% or more of global agreement to validate a transaction). For a financial transaction, the “truth” you’re trying to arrive at is relatively simple, and related to the ATM example earlier:
If this person has not spent this money in a previous transaction, this person still has the money
Essentially the bitcoin blockchain is a list of transactions that can be used to work out who owns what, based on a truthful account of what transactions have been made in the past. However, what happens when the transactions you’re writing to the blockchain aren’t so clear?
Provenance is a company that is attempting to increase trust in supply chains by writing data to an immutable blockchain to demonstrate that, for example, fish are caught sustainably. Where is the truth in this example? On one level you have a simple question: was this fish caught in a certain area of the ocean? That’s something a computer can answer - if the boat has a GPS you can link it to the transaction & automatically collect the data. But the question of “was this fish sustainably caught” is much more nuanced than that, meaning that we need more nuanced data. What about how much the fishermen are getting paid to catch the fish? Even if the person recording the data is paid well what if they’re employing workers on below minimum wage? What if - like Ryanair - on the surface it looks like they're being treated well but a complex system of companies means that they’re actually not getting the right employment rights?
Provenance give phones to fishermen to help them record data to the blockchain. Read more.
When you ask a sustainability consultant to assess whether fish is sustainably caught the research conducted won’t be binary; the data collected will include a value judgement based on factors other than the raw data. The data entered onto the blockchain, on the other hand, must be binary truth data that a computer can understand: this fish was either caught sustainably or it wasn’t.
The challenge for blockchain’s application into areas beyond financial transactions is exactly this fuzzy area. How can you create a rule based system when the rules in the real world are more flexible than we might like to admit?