The Nobel prize winner Ed Prescott introduced the term “chicken paper” to describe a certain kind of economics research article to the audience at ANU in a public lecture. For background, a macroeconomics paper commonly models the economy as a game (in the game theory sense) between households, sometimes adding the government, firms or banks as additional players. A chicken paper relies on three assumptions: 1) households like chicken, 2) households cannot produce chicken, 3) the government can provide chicken. Prescott’s point was to criticize papers that prove that the intervention of the government in the economy improves welfare. For some papers, such criticism on the grounds of “assuming the result” is justified, for some, not. This applies more broadly than just in macroeconomics.
One example that I think fits Prescott’s description is Woodford (2021, forthcoming in the American Economic Review), pages 10-11: “We suppose that units are unable to credibly promise to repay, except to the extent that the government allows them to issue debt up to a certain limit, the repayment of which is guaranteed by the government. (We assume also that the government is able to force borrowers to repay these guaranteed debts, rather than bearing any losses itself.)” The “units” that Woodford refers to are households, which are also the only producers of goods in the model. Such combined producer-consumers are called yeoman farmers and are a reasonable simplification for modelling purposes.
The inefficiency that the government solves in Woodford (2021) is the one discussed in Hirshleifer (1971) section V (page 568) that public information destroys mutually beneficial trading and insurance opportunities. In Woodford (2021), a negative shock to exactly one industry out of N in the economy occurs and becomes public at time 0 before trade opens. Thus the industries cannot trade contingent claims to insure against this shock. They are informed of the shock before trade. However, the government can make a transfer at time 0 to the shock-affected industry and tax it back later from all industries.
If the government also has to start its subsidizing and taxing after trade opens, it can still provide “retrospective insurance” as Woodford calls it by taxes and subsidies. Market-based “insurance” would also work: the affected industry borrows against the collateral of the government subsidy that is anticipated to arrive in the same period.
Baez-Mendoza et al (2021) claim that for rhesus macaques choosing which of two others to reward in each trial, „the difference in the other’s reputation based on past interactions (i.e., how likely they were to reciprocate over the past 20 trials) had a significant effect on the animal’s choices [odds ratio (OR) = 1.54, t = 9.2, P = 3.5 × 10^-20; fig. S2C]”.
In 20 trials, there are ten chances to reciprocate if I understand the meaning of reciprocation in the study (monkey x gives a reward to the monkey who gave x a reward in the last trial). Depending on interpretation, there are 6-10 chances to react to reciprocation. Six if three trials are required for each reaction: the trial in which a monkey acts, the trial in which another monkey reciprocates and the trial in which a monkey reacts to the reciprocation. Ten if the reaction can coincide with the initial act of the next action-reciprocation pair.
Under the null hypothesis that the monkey allocates rewards randomly, the probability of giving the reward to the monkey who previously reciprocated the most 10 times out of 10 is 1/1024. The p-value is the probability that the observed effect is due to chance, given the null hypothesis. So the p-value cannot be smaller than about 0.001 for a 20-trial session, which offers at most 10 chances to react to reciprocation. The p-value cannot be 3.5*10^-20 as Baez-Mendoza et al (2021) claim. Their supplementary material does not offer an explanation of how this p-value was calculated.
Interpreting reciprocation or trials differently so that 20 trials offer 20 chances to reciprocate, the minimal p-value is 1/1048576, approximately 10^-6, again far from 3.5*10^-20.
A possible explanation is the sentence “The group performed an average of 105 ± 8.7 (mean ± SEM) trials per session for a total of 22 sessions.” If the monkey has a chance to react to past reciprocation in a third of the 105*22 sessions, then the p-value can indeed be of the order 10^-20. It would be interesting to know how the authors divide the trials into the reputation-building and reaction blocks.
I am not a physicist, so the following may be my misunderstanding. Symmetry seems theoretically impossible, except at one instant. If there was a perfectly symmetric piece of matter (after rotating or reflecting it around some axis, the set of locations of its atoms would be the same as before, just a possibly different atom in each location), then in the next instant of time, its atoms would move to unpredictable locations by the Heisenberg uncertainty principle (the location and momentum of a particle cannot be simultaneously determined). This is because the locations of the atoms would be known by symmetry in the first instant, thus their momenta unknown.
Symmetry may not provide complete information about the locations of the atoms, but constrains their possible locations. Such an upper bound on the uncertainty about locations puts a lower bound on the uncertainty about momenta. Momentum uncertainty creates location uncertainty in the next instant.
Symmetry is probably an approximation: rotating or reflecting a piece of matter, its atoms are in locations close to the previous locations of its atoms. Again, an upper bound on the location uncertainty about the atoms should put a lower bound on the momentum uncertainty. If the atoms move in uncertain directions, then the approximate location symmetry would be lost at some point in time, both in the future and the past.