When you are trying to use a blockchain as a store of value or currency, as Bitcoin does, then the cost of Proof of Work mining may be acceptable for the reasons you mentioned.

However, being used as a currency is not the only thing that a blockchain can do, and many newer crypto projects are attempting to do other things. For example, you can use a blockchain to act as a company (a 'Distributed Autonomous Company', or DAC). The DAC can provide services to users, in exchange for fees, and attempt to make a profit. For example, the Bitshares project functions as a company that provides an exchange on the blockchain, in which you can trade assets such as gold, dollars, bitcoin, etc.

When you are attempting to run a company on a blockchain, profits matter, and that means that you need your fees generated from customers to exceed the cost to secure the network, and that means that you need to use a more efficient security algorithm than proof of work. Various Proof of Stake algorithms cost much less to maintain. If the goal of your blockchain is not to represent all of the money in the world, but rather to be an autonomous company performing a certain service to users, then maybe you do not need to go 100% all in on security, perhaps it makes more sense to try and make a profit using a cheaper consensus algorithm.

Finally, there are now a variety of different Proof of Stake algorithms. Not all of them suffer from the vulnerabilities of the original proof of stake, in which you can use old keys which used to control many coins in order to attack the network. For example, Ripple (Ripple Consensus algorithm), Bitshares (Delegated Proof of Stake), and NXT (Transparent Forging) algorithms do not suffer from this weakness, as far as I know.

I'm not sure what your background is; there are a number of books in philosophy of statistics that might be more accessible than Jaynes. I'm with the others - you should study some of the simpler foundations of statistics before diving into Bayesianism.

1) Luce and Raiffa's Games and Decisions (1989) introduces von Neumann and Morgenstern's axioms of objective utility theory and game theory; the book is geared towards serious social scientists.

2) Savage's The Foundations of Statistics (1954) is much harder than Luce and Raiffa, but it introduces an axiomatic system of subjective utility theory that complements objective utility theory well.

3) Fagin, Halpern, Moses and Vardi's Reasoning About Knowledge (2004) contains a reasonable introduction to statistics via the Kolmogorov axiomatization. It is suitable for technically minded philosophy students who have no background in measure theory, set theory or analysis.

4) Modern statistics builds heavily on calculus. You may want to brush up - I really like Bressoud's A Radical Approach to Real Analysis (2006). If you find yourself into the Kolmogorov axiomatization, you should also check out Bressoud's A Radical Approach to Lebesgue Measure Theory (2008).

These books should give you some perspective on the different traditions in philosophy of statistics and rational decision theory. After this Jaynes should make a lot of sense - he belongs to the "subjective" camp that Savage pioneered. However, it is important to understand when studying philosophy of statistics that there are multiple camps and they have different perspectives.