The Dan MacKinlay stable of variably-well-consider’d enterprises

Figure 1 Huh.

Status: Draft model, AI-assisted, not thoroughly checked.

Figure 1 An optionality-flavoured variant of economic decision theory (Peters 2019).

Figure 1 I noticed that there are a number of interesting sampling tricks for LLMs which seem related.

Figure 1 The frontend is the outermost layer of the stack — the human-facing surface, and the boundary that says what a harness — the loop running underneath — is not.

Figure 1 An internet search engine for humans returns an HTML page of links, on the reasonable assumption that a human will click a few of them to read.

Figure 1 It begins.

Figure 1 Making an LLM behave agentically is not just about the (large language) model but the harness around it.

WarningLLM-drafted — unverified Drafted by an LLM as a quick orientation aid: a telegraphic dictionary mapping renormalization-group (RG) and QFT vocabulary onto Bayesian-ML concepts.

Figure 1 Very WIP 🚧TODO🚧 Loss functions!

Figure 1 Coalition game and collective action problems, played between people and algorithms. 1 Collective action against a model A collective of users pools their data and coordinates how each member modifies it, to push a platform’s deployed learning al…

Figure 1 An ML formalization of interesting phenomena such as legibilizing and hyperstition, likely implicit in recommender dynamics, adversarial classification and some types of external validity, among other places.

This page is mostly AI slop, i.e.

Figure 1 Stub.

Placeholder.

Figure 1 Claude is a bad ghostwriter for me.

Dynomight wrote a disclosure document about how they use AI.

Figure 1 A spin-off from learning, since the link pile here is growing fast enough to warrant its own page.

Not actually my field.

Figure 1 I want my local language models to read my PDFs, analyse them, answer questions about them, and so on.

This page is mostly AI slop, i.e.

Causality, agency, decisions, learning is the motivation for this notebook.

Content warning: AI Slop awaiting human comprehension and review.

I want my phone to do small useful things for me — tally a thing I notice, nag me when I haven’t stretched, watch a sensor and remind me when something changes, that kind of thing.

TipAdjacent run-ups One of several notebooks I started on the same underlying problem.

tl;dr Australian unions are generally worth joining.

Figure 1 Consider the scaling laws across cities, economies, neural nets… Does it follow that in a competitive selection environment, ultimately, the world converges upon one leviathan?

Figure 1 Starting questions for better formulations of social belief. 1 Why do I believe things?

Figure 1 I am about to visit am visiting visited re-visiting Czechia.

Research-background notes.

Attention conservation notice: In hindsight this is a somewhat low signal post.

Environments and frameworks for multi-agent RL research.

Figure 1 Running code in the cloud without (explicitly) managing servers.

Figure 1 NoneA local app for local people It’s 2029.

Figure 1 This is the technical companion to A social platform for your neighbourhood, which lays out the social and institutional case for community-owned, local platforms.

Surely this analysis has been done before in the bowels of LessWrong.

Follow along while we work this out— danmackinlay/SOV or Sign up for updates.

Follow along while we work this out— danmackinlay/SOV, or Sign up for updates.

Figure 1 This is a companion to Who wants to found a friendly society?

We are very exercised about whether we have agency.

Figure 1 One massive project over the last few weeks has been founding the Alignment Journal, a new open-access journal for research on AI alignment and related topics.

Figure 1 A nature-inspired ensemble strategy for training neural nets that uses evolution-like approaches, even though we usually assume that scale can only be achieved via backprop.

Figure 1 It’s a nature-inspired ensemble strategy for optimization that uses evolution-like approaches.

Figure 1 If we have a big complicated system — a brain, a corporation, an ecosystem, an AI agent loop — where do we draw the line around “the thing”?

Figure 1 Category theory for systems.

The Wason selection task is a logic puzzle that uses a set of cards Figure 1.

Figure 1 An interesting alternative formulation that relaxes the Markovian assumptions in mainstream RL, though I don’t know much about it.

Learning to act clearly sounds like learning counterfactuals and interventions, right?

Figure 1 I’ll try to synthesize LLM research elsewhere.

Figure 1 I read Simon (1996) back in 2010, and it seemed cool.