Knowledge Problem

Eva runs a repair workshop and an unexpected job arrives. The inventory shows equipment, but a colleague knows more: a suitable machine has just become available, and a neighbouring workshop has the attachment they need. Hayek emphasises precisely this knowledge of particular times and places. A technical manual matters, but cannot by itself say what can be arranged this afternoon. Expertise and experience are not rivals here; a decision often needs both. [1]

Hayek uses tin as an example: new demand or a supply disruption may raise its price. Users then look for savings or substitutes without necessarily knowing the original cause. Prices can connect distant people’s decisions without everyone reading the same report. But a price does not tell the whole story, and responses need not be perfect. Economic Calculation, comparing costs and plans through money prices, uses that signal without turning it into complete knowledge. [1]

A report lists several suitable machines in the region. One is far away, another awaits repair, and another is available only next week. The total is not necessarily wrong; it simply does not answer Eva’s question about finishing today. Grouping data into categories loses some distinctions. An overview helps us understand the situation, but a decision still requires checking whether location, condition or timing changes the options actually available. [1]

In Economics and Knowledge, Hayek distinguishes compatible plans from an explanation of how people arrived at them. Eva expects a part to arrive; the supplier expects her to accept it. Assuming they both know everything correctly does not explain their coordination. Our example requires examining the order confirmation, revised delivery date or delay report, and whether people adjusted their plans accordingly. A model with complete information can be useful, but the actual learning process still needs explaining. [2]

Software shows Eva a part in stock. The record may predate another customer’s purchase, or refer to a different version. Applied to a database, Hayek’s question becomes practical: where did this information come from, when was it updated, and what exactly does it describe? Faster software can process entered data better without thereby establishing its completeness or usefulness. This is not a rejection of databases; it distinguishes stored data from the knowledge needed for a particular decision. [1] [3]

The mechanic may know the machine well without knowing that a part has become dearer or a supplier faces transport problems. Decentralization, distributing decisions among several participants, also requires connections between them. Hayek therefore links local knowledge with further signals, particularly prices. Being closest to the job does not make Eva infallible. She needs ways to check information, revise her plan and learn from what went wrong. [1] [2]

The workshop can easily count orders, but recording lost customer trust or an employee’s experience with an unusual fault is harder. In The Pretence of Knowledge, Hayek warns against treating only what can be measured numerically as important. A precise spreadsheet does not justify a precise forecast. It helps to distinguish observations, assumptions and remaining unknowns. Measurement does not become worthless; its limits need to be acknowledged. [3]

Eva can accept Bitcoin and verify the payment under its network’s rules. For example, she can check whether the transaction is included in a block, a group of transactions accepted under the network’s rules. That does not tell her whether the spare part will arrive or the customer will like the repair. A verifiable payment record and market knowledge serve different purposes. A distributed network does not guarantee complete business information or flawless decisions. Paying in bitcoin still leaves Eva needing a call to the supplier, the mechanic’s experience and the ability to change her plan. [1] [2] [4]