Weak/Medium- hypothesis that security of basic needs will increase willingness to take on volunteer work. Demonstrated increase in voluntary care labour in universal basic income trials

Weak/Medium - Evidence that this effect is rare from universal basic income trials. Hypothesis that most quitting will occur in jobs judged to be morally unfulfilling (e.g. nurses unlikely to quit their jobs). Hypothesis that threat of quitting will lead to improved workplace conditions.

Weak/Medium - Likely to occur if the promise is successful in providing basic needs. Frequent examples of basic needs coverage resulting in reduced crime, lowering incarceration rates and freeing up resources for other areas. Hypothesis that facilitating military deescalation will reduce military spending, freeing up resources for other areas

Medium - Frequent examples of basic needs coverage resulting in reduced crime. Untested in specific case of the promise

Reduced effectiveness of other methods for providing food, water, peace and shelter

Weak - hypothesis that providing food, water, shelter and peace will slightly reduce effectiveness of other charities working on these issues, since there are fewer acute cases. Scale of the issues is much larger than charities' abilities to solve them, indicating that 'competition' will be minimal and this effect will be small

Weak - hypothesis that providing food, water, shelter and peace will increase incentives for innovators to innovate, since they can bootstrap for longer to reduce share dilution from pre-seed investment, and since risks are covered by a social safety net

Average annual cost per employee (USD)

% reduction of hunger for those promisers

% reduction of water inaccess for those promisers

Explanation of maths used to calculate table above

The following differential equation can be used to calculate dP/dt, the rate of change of promisers per day

This differential equation can be solved to give the following solution

Where N is the initial number of promisers at t=0 (in this case 100 promisers)

This can be used to solve for t, so that we can determine how long it takes to get to a certain number of promisers

You can see that the formula changes slightly in the final column. This is because the value of Rp changes for this section

Guarantee an average person freedom from:

Per standard promiser (first 2 billion of population)

Conservative estimate based on my own experience of being able to recruit a promiser every two hours. Likely to be higher over time as media exposure increases

Defection rate of promisers (first 2 billion of population)

Estimate - requires testing. Conservatively high estimate. Hypothesis of lower actual rate, due to minimal advantage of defecting from the promise, as those who feel like helping is hard are simply required to enlist more promisers, requiring minimal benefit

Defection rate of promisers (after 2 billion population)

Estimate - lower defection rates due to higher social pressure

Sharing rate of promisers (first 2 billion of population)

I asked several promisers how frequently they thought they would likely recruit new promiser, at a rate that they thought would be sustainable over several years. The answers averaged out at around one new promiser every two weeks, or 0.07 promisers per day. Due to intention-action gaps, the real figure will likely be lower. Also due to founder effect bias, where the first promisers are more enthusiastic than average, the real value will likely be lower. I made this estimate 0.015 as a conservative guess.

Sharing rate of promisers (after 2 billion population)

Estimate that the rate of sharing will be slower for the last 2 billion people, since people with higher natural enthusiasm will have already taken the promise. This effect will be balanced by greater social pressure as a high proportion of the population becomes promisers