Use the live research desk
Use the experiment workspace to plan several approaches to one problem. Matching population, outcome unit, currency and time horizon groups saved scenarios for comparison. Enter evidence and uncertainty before interpreting cost per expected outcome. Blank inputs stay unknown. These scenario bounds are not confidence intervals, effectiveness scores or forecasts validated against results.
Your workspace stays in this browser. Save records, edit them or fork frozen versions, export JSON, a research memo or CSV, and import a backup with an explicit replacement step. Deleting or clearing offers an undo while the page remains open. No notes are submitted to Charitology. The institutional goals below remain the program’s longer-term charter.
What it is
People give in many ways. They send cash directly, fund advocacy for a change in policy, pay for research, join mutual aid with their neighbors, volunteer, invest for impact and put up prizes. Choosing between two cash programs is one kind of question. Choosing between cash, advocacy and a prize for the same problem is another. The Methods Lab would study the second kind.
How it works
The lab would take one problem at a time and ask how each method would go after it. For each method it would record the evidence, what it costs to find out whether the method worked, how long results take, who decides how the money is used and where the work acts on the depth scale, from relief to source.
The evidence is uneven, and the lab would say so. Some methods have been studied in depth: a 2016 review by ODI drew together 15 years of studies of cash transfers in 30 low- and middle-income countries. For others, the lab would start by gathering what evidence exists. Charitology knows of no study that puts all seven side by side on the same problem.
Why it matters for the system
Each method tends to fit some moral frameworks better than others. Cash lets the people who receive it choose. Advocacy aims at the rules. Mutual aid starts from relationships. A giver who only ever uses one method sees giving through one lens, which is the Single Lens applied to tools instead of causes.
Methods also differ in how easy their results are to see. Cash delivered and services provided are easy to count. Advocacy and research often act further upstream, and their results can take years to show. If evidence that is easy to collect becomes the only test, money drifts toward the methods that are easiest to measure, which is one way the Symptom Trap could work. The lab would keep “hard to measure” and “doesn’t work” apart.
Risks and limits
Comparing methods means comparing different kinds of evidence, and there is no neutral scale for that. The lab would publish its reasoning and its weights so others can rerun the comparison with their own.
What success looks like
Within five years, the lab has compared all seven methods on at least three problems, and a giver choosing how to give can see each method’s evidence, depth and trade-offs in one place.
Compare ways of approaching one problem
Design alternatives across seven methods. Give them the same outcome unit, population, currency and time horizon. Record evidence and missing information before interpreting the numerical scenarios.
Direct cash
What can recipients choose for themselves, and which shared goods need another route?
Policy advocacy
Which rule could change, who else must act and how would you assess contribution?
Research
Whose decision would an answer change, and what happens if the result is negative?
Mutual aid
Who belongs to the network, who is missed and how are decisions shared?
Volunteering
Which skill is needed and what does coordination cost the organization?
Impact investing
What changes because of this capital that an ordinary investor would not fund?
Prizes
What result can be judged fairly and who can afford to work before payment?
No method is assigned an effectiveness score by Charitology. All numerical inputs below are your assumptions. Source depth describes where the proposed work acts; it does not decide which method is better.
Scenario comparison
Save approaches below, then compare only records sharing the same scope. A current draft is shown separately when it matches.
Model: expected outcome units = chance of success × units if successful. Failure is assumed to produce zero of the chosen outcome; harms and other outcomes stay in the written appraisal. Cost per expected unit runs from lower cost ÷ upper expected units to upper cost ÷ lower expected units. A zero lower expected outcome makes the upper cost unbounded. These are assumption bounds, not confidence intervals or forecasts validated by data.
Methods experiment workspace
New recordWrite a testable approach, preserve uncertainty and save several alternatives. Blank numeric fields stay unknown, and a reported zero stays zero. The comparison is a planning model, not a published finding about the methods.
Your work stays in this browser. Export a backup to move it elsewhere. Entries are your research notes, not verified findings or ratings.