Michael Gronager [ARCHIVE] on Nostr: 📅 Original date posted:2013-11-15 📝 Original message:-----BEGIN PGP SIGNED ...

📅 Original date posted:2013-11-15
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Hi Peter,

Love to see things put into formulas - nice work!

Fully agree on the your fist section: As latency determines maximum
block earnings, define a 0-latency (big-miner never orphans his own
blocks) island and growing that will of course result in increased earnings.

So build your own huge mining data center and you rock.

However, that is hardly the real work scenario today. Instead we have
pools (Huge pools). It would be interesting to do the calculation:

Q = Total pool size (fraction of all mining power)
q = My mining power (do.)
e = fraction of block fee that pool reserves

It is pretty obvious that given your formulas small miners are better
off in a pool (can't survive as solo miners), but there will be a
threshold q_min above which you are actually better off on you own -
depending also on e. (excluding here all benefits of a stable revenue
stream provided by pools)

Next interesting calculation would be bitcoin rate as a function of pool
size, I expect a sharp dip somewhere in the 40%s of hardware controlled
by one entity ;)

Finally, as you mention yourselves, qualification of the various
functions is needed. This could e.g. suggest if we are like to get 3 or
10 miners on the long run.

And now for section 2. You insert a definition of f(L) = a-bL. I think
the whole idea of letting f depend on L is superfluous. As a miner you
are always free to choose which transactions to include. You will always
choose those with the biggest fee, so really it is only the average fee
that is relevant: f(L) = c. Any dependence in L will be removed by the
reshuffeling. To include an extra transaction will require either that
it has a fee larger than another (kicking that out out) or that it has a
fee so large that it covers for the other transaction too. Also recall
that there is a logical minimum fee (as I have already shown), and a
maximum optimal block size - that is until the bounty becomes 0 (which
is where other effects kick in).

> Here's what I've got to date. The first two sections is just a
> relatively simple proof that mining is more profitable as centralization
> increases under any circumstance, even before any real-world factors are
> taken into account. (other than non-zero latency and bandwidth) Nice
> homework problem, and neat that you can easily get a solid proof, but
> academic because it doesn't say anything about the magnitude of the
> incentives.
>
> The latter part is the actual derivation with proper model of
> supply-and-demand for fees. Or will be: while you can of course solve
> the equations with mathematica or similar - getting a horrid mess - I'm
> still trying to see if I can simplify them sanely in a way that's
> step-by-step understandable. Taking me longer than I'd like; sobering to
> realize how rusty I am. That said if any you do just throw it at
> Mathematica, looks like you get a result where the slope of your
> expected block return is at least quadratic with increasing hashing
> power. (though I spent all of five minutes eyeballing that result)
>
>
> \documentclass{article}
> \usepackage{url}
> \usepackage{mathtools}
> \begin{document}
> \title{Expected Return}
> \author{Peter Todd}
> \date{FIXME}
> \maketitle
>
> \section{Expected return of a block}
> \label{sec:exp-return-of-a-block}
>
> Let $f(L)$, a continuous function,\footnote{Transactions do of course give a
> discontinuous $f$. For a large $L$ the approximation error is negligible.} be
> the fee-per-byte available to a rational miner for the last transaction
> included in a block of size $L$. $f(L)$ is a continuous function defined for $L
> \ge 0$. Supply and demand dictates that:
>
> \begin{equation}
> f(L) \ge f(L+\epsilon) \label{eq:f-increases}
> \end{equation}
>
> A reasonable example for $f$ might be $f(L) = kL$, representing the demand side
> of a linear supply and demand plot. For a block of size $L$ that is optimally
> filled with transactions the value of those fees is just the integral:
>
> \begin{equation}
> E_f(L) = \int_0^L f(l)\,dl
> \end{equation}
>
> Let $P(Q,L)$, a continuous function, be the probability that a block of size
> $L$ produced by a miner with relative hashing power $Q$ will be orphaned.
> Because a miner will never orphan their own blocks the following holds true:
>
> \begin{equation}
> P(Q,L) \le P(Q + \epsilon,L) \label{eq:p-increases}
> \end{equation}
>
> Similarly because larger blocks take longer to propagate and thus risk getting
> orphaned by another miner finding a block at the same time:
>
> \begin{equation}
> P(Q,L) \ge P(Q,L + \epsilon)
> \end{equation}
>
> By combining $P(Q, L)$, $E_f(L)$ and the inflation subsidy $B$, gives us the
> expected return of a block for a given size and hashing power:\footnote{Note
> how real world marginal costs can be accommodated easily in the definitions of
> $f$ and $B$.}
>
> \begin{equation}
> E(Q,L) = P(Q,L)[E_f(L) + B]
> \end{equation}
>
> The optimal size is simply the size $L$ at which $E(Q, L)$ no longer increases:
>
> \begin{equation}
> \frac{d}{dL}\big[E(Q, L(Q))\big] = 0
> \end{equation}
>
> We will define the function $L(Q)$ as the optimal value for a given $Q$. A
> miner creating optimal blocks will thus have an expected return per block found
> of $E'(Q)=E(Q,L(Q))$. Note how this definition is per unit hashing power by
> virtue of being per block found.
>
>
> \section{Optimal return $E'$ vs. hashing power $Q$}
>
> We want to know if a large miner has a larger return for a given amount of
> hashing power. We do this by taking the derivative with respect to $Q$ of the
> expected return given optimal strategy:
>
> \begin{align*}
> \frac{d}{dQ}\big[E'(Q)\big] &= \frac{d}{dQ}\big[P(Q,L(Q))\big]\big[E_f(L(Q)) + B\big] + P(Q,L(Q))\frac{d}{dQ}\big[E_f(L(Q))\big] \\
> &= \frac{dL(Q)}{dQ}\Big[\frac{dP(Q,L(Q))}{dQ}\big[E_f(L(Q)) + B\big] + P(Q,L)\frac{dE_f(L(Q))}{dQ}\Big]
> \end{align*}
>
> We know that $L(Q)$, $E_f$, $P$, and $B$ are all $\ge 0$. Thus for $dE'/dQ$ to
> be negative requires either $dL/dQ$ to be negative, or for $dL/dQ$ to be
> positive and one of $dP/dQ$ or $dE_f/dQ$ negative.
>
> Suppose $dP/dQ$ negative and $dL/dQ$ positive:
>
> \begin{align}
> \frac{dL(Q)}{dQ} > 0 &\implies L(Q + \epsilon) > L(Q) \notag \\
> \frac{dP(L(Q))}{dQ} < 0 &\implies P(Q + \epsilon, L(Q + \epsilon)) < P(Q, L(Q)) \label{eq:dl-pos-dp-neg}
> \end{align}
>
> But that contradicts our definition \eqref{eq:p-increases} of $P$ as continuous
> and increasing. Suppose instead that $dE_f/dQ$ is negative and $dL/dQ$
> positive:
>
> \begin{align}
> \frac{dL(Q)}{dQ} > 0 &\implies L(Q) < L(Q + \epsilon) \notag \\
> \frac{dE_f(L(Q))}{dL} < 0 &\implies E_f(L(Q)) > E_f(L(Q + \epsilon)) \notag \\
> &\implies \int_0^{L(Q)} f(l)\,dl > \int_0^{L(Q+\epsilon)} f(l)\,dl \notag \\
> &\implies f(l) < 0 \label{eq:dl-pos-de-neg}
> \end{align}
>
> Again we have a contradiction with our definition \eqref{eq:f-increases} of
> $f$. Finally suppose $dL/dQ$ is negative:
>
> \begin{align}
> \frac{dL(Q)}{dQ} < 0 &\implies L(Q) > L(Q + \epsilon) \notag \\
> &\implies P(Q + \epsilon, L(Q + \epsilon)) < P(Q, L(Q)) \notag \\
> &\implies \frac{dP(Q, L(Q))}{dQ} < 0 \notag \\
> &\implies \frac{dL(Q)}{dQ}\frac{dP(Q, L(Q))}{dQ} > 0 \label{eq:dl-neg-dp-neg} \\
> &\implies E_f(L(Q + \epsilon)) < E_f(L(Q)) \implies \frac{dE_f(L(Q))}{dQ} < 0 \notag \\
> &\implies \frac{dL(Q)}{dQ}\frac{dE_f(L(Q))}{dQ} > 0 \label{eq:dl-neg-de-neg}
> \end{align}
>
> Even if $dL/dQ$ is negative \eqref{eq:dl-neg-dp-neg} and
> \eqref{eq:dl-neg-de-neg} show that $dE'/dQ > 0$. In conjunction with
> \eqref{eq:dl-pos-dp-neg} and \eqref{eq:dl-pos-de-neg} we prove that increased
> hashing power always leads to increased return on investment per unit hashing
> power.
>
>
> \subsection{Real-world implications to centralization}
>
> While the author has shown that they still remember first-year, is this result
> relevant?
>
> The proof holds regardless of what any of the functions actually are, provided
> that they meet the requirements set out in section
> \ref{sec:exp-return-of-a-block}. The requirements are met by any reasonable
> real-world scenario\footnote{Negative fees are not reasonable!}, and show an
> incentive for mining to centralize even in an ideal situation where all miners
> are on a level playing field and have no fixed costs.
>
> However the proof is abstract, and doesn't tell us anything about how strong
> that pressure is; it may be insignificant enough to be outweighed by effects
> such as social pressure.
>
> We need to investigate $dE'/dQ$ in detail.
>
>
> \section{Detailed derivation of of $P(Q,L)$}
>
> \subsection{Assumptions}
>
> The difficulty is assumed to be in a steady state condition and the
> percentage of hashing power for any given miner is fixed. Unconfirmed
> transactions are assumed to be known to all miners, giving everyone an
> equal opportunity of mining any given transaction.
>
> We assume that the graph of all Bitcoin miners is fully connected and
> that the bandwidth, $1/k$, and latency, $t_0$, is identical for all
> connections and unchanging. We assume that miners always attempt to
> build upon the first block they see on the longest chain known to them,
> and when they find a block, they always broadcast it to all other miners
> simultaneously. From that we see that the time taken for a block of size
> $L$ to propagate to $100\%$ of the hashing power is simply:
>
> \begin{equation}
> t(L) = t_0 + kL
> \end{equation}
>
>
> \subsection{Analysis}
>
> When miner $Q$ finds a block during the condition of full consensus the
> outcomes can be described by the following state tree. The numbers in brackets
> are the "scorecard" of blocks found by $Q$ and all other miners should a given
> state be reached:
>
> \begin{description}
>
> \item[1)] No other block is found prior to full propagation. (1:0)
>
> \item[2)] $Q$ finds another block prior to full propagation. (2:0)
> \begin{description}
> \item[2.1)] $Q$'s second block is not orphaned. (2:0)
> \item[2.2)] $Q$'s second block is orphaned. (2:3)
> \end{description}
>
> \item[3)] $(1-Q)$ finds another block prior to full propagation. (1:1)
> \begin{description}
> \item[3.1)] $(1-Q)$'s block is orphaned. (2:1)
> \item[3.2)] $(1-Q)$'s block is not orphaned. (1:2)
> \end{description}
> \end{description}
>
> Miner $Q$ wins if states $1$, $2.1$, or $3.1$ are reached. Though it is
> possible to derive an equation for $P$ that accurately models possible states -
> the author did exactly that in a fit of madness - the resulting equation is
> unwieldly and offers no additional insight.
>
> We want to end up with a $dE'/dQ$ that captures second order effects. Since
> $L(Q)$ and thus $E'(Q)$ will depend on $Q$ our approximation of $P$ should be
> such that $dP/dQ$ is at least linear.
>
> With $\lambda$ as the block interval the probabilities of reaching states $1$,
> $2$, and $3$ are as follows:
> \begin{align}
> p_1 &= 1 - \frac{t}{\lambda} \\
> p_2 &= \frac{t}{\lambda} Q \\
> p_3 &= \frac{t}{\lambda} (1-Q)
> \end{align}
>
> We could assume that states $2$ and $3$ both lead to the block being orphaned,
> thus giving us:
> \begin{equation}
> P(Q, L) = 1 - \frac{t}{\lambda} = 1 - \frac{t_o + kL}{\lambda}
> \end{equation}
>
> However this gives us a linear $E(Q, L)$, linear $L(Q)$, and thus only a
> quadratic $E'(Q)$. We need at least one more state in our model; state $2.1$ is
> a good choice. Reaching state $2.2$ is exceptionally improbable - the miners
> $(1-Q)$ have to find three blocks in time $t$ - so ignoring state $2.2$ and
> thus using the probability for state $2$ instead has negligible impact on the
> model. Meanwhile state $3$ requires that state $3.1$ be used directly and would
> result in a third-order terms in $P$ when treating state $3$ as an always loss
> is a conservative lower-bound.
>
> This gives us:
> \begin{align}
> P(Q, L) &= p_1 + p_2 = 1 - \frac{t}{\lambda} + \frac{t}{\lambda} Q = 1 - (1-Q)\frac{t}{\lambda} \notag \\
> &= 1 - (1-Q)\frac{t_o + kL}{\lambda}
> \end{align}
>
>
> \subsection{Detailed derivation of E'(Q)}
>
> Some preliminaries:
>
> \begin{align}
> \frac{dP(Q,L)}{dL} &= -(1-Q)\frac{k}{\lambda} \\
> \notag\\
> \frac{dE(Q,L)}{dL} &= \frac{dP(Q,L)}{dL}\big[E_f(L) + B\big] + P(Q,L)\frac{dE_f(L)}{dL} \notag\\
> &= \frac{dP(Q,L)}{dL}\big[E_f(L) + B\big] + P(Q,L)\,f(L)
> \end{align}
>
> We're not going to get very far without a definition for $f$ so we'll use a
> simple linear demand model:
>
> \begin{align}
> f(L) &= a - bL \\
> E_f(L) &= aL - \frac{1}{2}bL^2
> \end{align}
>
> Now we set $dE/dL=0$ and solve for $L$. To simplify the problem we will consider the no-subsidy, $B=0$ case:
>
> \begin{align}
> 0 &= \frac{dP(Q,L)}{dL}E_f(L) + P(Q,L)\,f(L) \\
> &= -(1-Q)\frac{k}{\lambda}\big[aL - \frac{1}{2}bL^2] + \big[1 - (1-Q)\frac{t_o + kL}{\lambda}\big](a - bL) \\
> \end{align}
>
>
> \end{document}
>
>>> Luckily the fork frequency and the average block size are easily
>>> measurable. blockchain.info keeps historical graphs of number of
>>> orphaned blocks pr day
>>
>> Are those stats accurate? Have any pool operators at least confirmed that the
>> orphaned blocks that blockchain.info reports match their own records?
>>
>> My gut feeling is to relay all orphaned blocks. We know that with a high
>> investment and sybil attack as blockchain.info has done you can have better
>> awareness of orphaned blocks than someone without those resources. If having
>> that awareness is ever a profitable thing we have both created an incentive to
>> sybil attack the network and we have linked profitability to high up-front
>> capital investments.
>>
>> On those grounds alone I will argue that we should relay all orphans to even
>> the playing field. If there is a circumstance where we do not want the attacker
>> to have that knowledge we have failed anyway, as blockchain.info's sybil attack
>> on the network clearly shows.
>
> Agreed.
>

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