# (Goals and planning)

## "Economics+ for EA+ and vice/versa"&#x20;

### Who is this for and why

1. ***EA's and fellow-travelers:***

* The principles and practice of effective altruism are closely connected to the principles of Economics. EA's engage economic concepts, arguments, and theoretical and empirical "results" throughout their work. However, these **principles are sometimes misunderstood or mis-stated,** sometimes alluded to but not considered in depth. The **terminology** itself can sometimes be muddled between EA, rationalist, decision theory, computer science, and academic economics communities. This leads to confusion and barriers to engagement.
* &#x20;I also get the sense that EA's are **somewhat over-optimistic and over-broad** about 'what Economics tells us' (possible example: GiveWell on 'log utility'); often a result that holds under only very-specific conditions is stated as a generally accepted truth.  On the other hand
* &#x20;Some useful areas of Economics seem neglected in EA, e.g., (my own impressions):
  * "Basic" supply-demand-production systems, aggregation, empirical general and partial equilibrium models ... are very relevant to considering policy interventions, especially in animal welfare.
    * Aside: This has been  neglected by academic economists because its seen as non-deep, and neglected by private-sector/government economists because there is no established 'animal welfare policy audience'
    * Some progress may be happening: see the [Stanford conference on the Economics of Animal Welfare.](https://economics.stanford.edu/site/paper-submission)
  * Some decision-theoretic concepts and models of preferences... with interesting implications for 'social preferences' and 'communicating preferences to machines'&#x20;
  * Non-utilitarian preference relations like 'lexicographic' ... seems neglected in the discussion of population ethics (it's OK to have 'non-continuity', perhaps)&#x20;
  * Social Welfare Functions and Social Choice
  * Comparability of 'utility' across individuals and time, revealed preference&#x20;
  * Economics (esp. field and natural experiments, revealed preferences) of eliciting risk and time preferences&#x20;
  * Some work in 'the Economics of other-regarding behavior' (charitable giving and 'crowding out', consumer altruism...) (But I do think some parts of this literature are stuck in a trap of confusion about the application of models like 'warm glow').&#x20;

*2. **Economists & co.***

* Understanding how individuals and firms make choices,  how these aggregate in market (and other settings) is the original 'what' of Economics. The original 'why do we care about this' is  'to understand what will achieve the best outcomes for humanity (and perhaps beyond). But IMO it has been somewhat waylaid by the desire to demonstrate cleverness and rigorous extensions of existing models. It has also been distracted by parochialism: to the extent there is a 'policy audience', it is typically the US Government. &#x20;
  * Economists will benefit from an approach that returns to the 'global welfare' question first and foremost, and benefit from engaging with the EA community, which is *practically* trying to achieve these goals
* This 'integration' will bring in interesting concepts from Philosophy and Decision Science that Economists may have neglected
* EA questions and goals provide a new *research agenda* and the opportunity to apply core concepts and tools from Economics in ways that may be *more directly relevant* than the more mainstream ('fix the US economy'...) targets. Some brainstorms on this (needs clarification)...
  * Preference axioms (transitivity etc), VnM Axioms, impossibility theorems etc:
    * Application to 'social preferences' and 'aggregated social preferences'
    * Time discounting: weigh present/future
    * Population ethics; weigh definite/possible individuals
    * Uncertainty and 'preference over outcomes versus over impact
  * Defining preferences and constraints: tools for aligning AI?
  * The 'aggregation from individual optimization problems' may be unreliable for predicting chaotic human systems, but more relevant for 'aligning AI'
  * Measuring and assessing 'tradeoffs between income gains at different levels' (e.g., for GiveWell and GiveDirectly) with different empirical and theory-driven approaches
  * Aggregating social welfare functions and other social preference formulations with epistemic and moral uncertainty&#x20;
  * Implications of GE models for animal welfare interventions; a new set of value measurements and possible interventions; not just the 'market failures' approach&#x20;

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## Making this work: implementation

## Opportunities, threats, important issues
