Aaron Graifman

• Aaron Graifman 4 Mar 2024 16:58 UTC

  1 point

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  in reply to: CAISID’s comment on: Ex­plor­ing an EA Equiv­a­lent to Job Si­mu­la­tion Web­sites for Skills and Ap­ti­tude Devel­op­ment & Seek­ing In­sights on EA-Aligned Organizations

  Thank you. When you say “regulation tasks” what are you thinking of more specifically?

[Question] Ex­plor­ing an EA Equiv­a­lent to Job Si­mu­la­tion Web­sites for Skills and Ap­ti­tude Devel­op­ment & Seek­ing In­sights on EA-Aligned Organizations

• Aaron Graifman 13 Feb 2024 16:45 UTC

  9 points

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  on: On be­ing an EA for decades

  Thank you for sharing this journey. In addition, I will be using the STOPP routine now more often. I implemented it today when I found myself getting overwhelmed by the calls I had to make today and it helped guide me back on track.
  
  This also helped me gain a feel for how the community has grown.
  
  This is inspiring

• Aaron Graifman 12 Dec 2023 18:50 UTC

  1 point

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  in reply to: Joel Tan’s comment on: Is There a Pro­ject Track­ing Sys­tem in EA?

  Thank you Joel for pointing me in the right direction. You make a good point on not wanting to prematurely write off research findings
  In response to the challenges in cause/​intervention prioritization and the benefits of research duplication:

  • Bayesian Updating for Research Analysis: Implement a Bayesian updating system to continuously revise research certainty, accommodating new data and insights.

  • Visual Credibility Indicators: Use a color gradient in a table format to represent research certainty levels, making it easier to interpret findings at a glance.

  • Research Depth and Uncertainty Metrics: Track the extent and depth of research, focusing on areas with high uncertainty or potential for further exploration.

  • Estimating Further Research Potential: Assess the potential for additional insights in each research area, similar to estimating untapped resources in land surveying.

  • Goal: To refine research prioritization, recognize the value of duplication for verification, and avoid premature dismissal of ideas based on limited data.

  Potential Pitfalls:

  • Over-reliance on Quantitative Metrics: Solely relying on quantitative metrics might overlook qualitative aspects of research, leading to incomplete evaluations.

  • Misinterpretation of Data: The color gradient system could be misinterpreted if not accompanied by clear guidelines, leading to incorrect conclusions.

  • System Complexity: Implementing a Bayesian updating system could be complex and require significant resources for accurate and effective functioning.

  • Bias in Data Input: The system’s effectiveness is contingent on the quality and neutrality of the input data, with biased inputs leading to skewed outcomes.

  • Ignoring Contextual Factors: The system might not fully account for contextual factors affecting research, such as socio-economic or cultural aspects.

[Question] Is There a Pro­ject Track­ing Sys­tem in EA?

[Question] Are U.S. Poli­ti­ci­ans In­tel­lec­tu­ally Ri­gor­ous Be­hind the Scenes or Do Public De­bates Ac­cu­rately Reflect Their Un­der­stand­ing?