Results for “model this” 3452 found
A simple model of AI and social media
One MR reader, Luca Piron, writes to me:
I found myself puzzled by a thought you expressed during your interview with Professor Haidt. In particular, from my understanding you suggested that in the near future AI will be able to sum up the content a user may want to see into a digest, so that they can spend less time using their devices.
I think that is a misunderstanding of how the typical user experiences social media. While there surely are some brilliant people such as the young scientists you described during the episode who use social media only to connect with peers and find valuable information, I would argue that most users, alas including myself, turn to social media when seeking mindless distraction, when bored or maybe too tired to read of watch a film. Therefore, having a digest will prove unsatisfactory. What a typical user wants is the stream of content to continue.
I think these are some of the least understood points of 2024. Let us start with the substitution effect. The “digest” feature of AI will soon let you turn your feeds into summaries and pointers to the important parts. In other words, you will be able to consume those feeds more quickly. In some cases the quality of the feed experience may go up, in other cases it may go down (presumably over time quality of the digest will improve).
We all know that if tech allows you to cook more quickly (e.g., microwave ovens), you will spend less time cooking. That is true even if you are “addicted” to cooking, if you cook because of social pressures, if cooking puts you into a daze, or whatever. The substitution effect still applies, noting that in some cases the new tech may make the cooked food better, in other cases worse. In similar fashion, you will spend less time with your feed, following the advent of AI feed digests.
Somehow people do not want to acknowledge the price theory aspect of the problem, as they are content to repeat the motives of young people in spending time with their feeds. (You will note there is the possibility of a broader portfolio effect — AI might liberate you from many tasks, and you could end up spending more time with your feed. I’ll just say don’t bet against the substitution effect, it almost always dominates! And yes for addictive goods too. In fact those demand curves usually don’t look any different.) No one has to be a young genius scientist for the substitution effect to hold.
Note that a majority of U.S. teens report they spend about the right amount of time on social media apps (8% say “too little time”) and they are going to respond to technological changes with pretty normal kinds of behavior.
I think what has in fact happened is that commentators have read dozens of MSM articles about “algorithms,” and mostly are not following very recent tech developments, including in the consumer AI field. Perhaps that is why they have difficult processing what is a simple, straightforward argument, based on a first-order effect.
Another general way of putting the point, not as simple as a demand curve but still pretty straightforward, is that if tech creates a social problem, other forms of tech will be innovated and mobilized to help address that problem. Again, that is not a framing you get very often from MSM.
The AI example is also a forcing one when it comes to motives for spending time with social media feeds. Many critics wish to have it both ways. They want to say “the feed is no fun, teenagers stick with the feed because of social pressures to be in touch with others, but they ideally would rather do something else.” But when a new technology allows them to secede from feed obsession to some degree, (some of) those same critics say: “They can’t/won’t secede — they are addicted!” The word “dopamine” is then likely to follow, though rarely the word “fun.”
It is better to just start by admitting that the feed is fun, and informative, for many teenagers and adults too. Of course not everything fun is good for you, but the “social pressure” verbal gambit is a slight of hand to make social media sound like an obvious bad across all margins, and a network that needs to be taken down, rather than something we ought to help people manage better, at the margin. If it really were mainly a social pressure problem, it would be relatively easy to solve.
For many teens, both motives operate, namely scrolling the feed is fun, and there are social pressures to stay informed. The advent of the AI digest will allow those same individuals to cut back on the social pressure obligations, but keep the fun scrolling. Again, a substitution effect will operate, and furthermore it will nudge individuals away from the harmful social pressures and closer to the fun.
As Katherine Boyle pointed out on Twitter, a lot of this debate is being conducted in terms of 2016 technology. But in fact we are in 2024, not far from the summer of 2024, and soon to enter 2025. Beware of regulatory proposals, and social welfare analyses, that do not acknowledge that fact.
In the meantime, please do heed the substitution effect.
Algorithmic Collusion by Large Language Models
The rise of algorithmic pricing raises concerns of algorithmic collusion. We conduct experiments with algorithmic pricing agents based on Large Language Models (LLMs), and specifically GPT-4. We find that (1) LLM-based agents are adept at pricing tasks, (2) LLM-based pricing agents autonomously collude in oligopoly settings to the detriment of consumers, and (3) variation in seemingly innocuous phrases in LLM instructions (“prompts”) may increase collusion. These results extend to auction settings. Our findings underscore the need for antitrust regulation regarding algorithmic pricing, and uncover regulatory challenges unique to LLM-based pricing agents.
That is a new paper by Sara Fish, Yannai A. Gonczarowski, and Ran I. Shorrer. The authors are running too quickly into their policy conclusion there (how about removing legal barriers to free entry in many cases? not worth a mention?), but nonetheless very interesting work. Via Ethan Mollick.
Teaching the Super Simple Solow Model
Here’s my talk on teaching the super simple Solow model which Tyler and I present in our principles textbook, Modern Principles. Here are the slides.
Strong AI and the O-Ring model
Let’s say the Sumerians were gifted strong AI, simply as an exogenous shock to a historical model. Could they put it to much use? Electricity would be one immediate problem, but not the only problem.
Or give strong AI to a caveman.
Thomas Edison had electricity, but how much could he do with strong AI? Lord Asquith? Adlai Stevenson?
Where exactly are we in this historical sequence?
Teaching the Solow Model
The Solow model is a foundational model for understanding economic growth. Yet it’s typically not taught to principles students because it’s considered too difficult. In Modern Principles, however, Tyler and I develop a super simple version of the model that is fun to teach and accessible to students of all levels. I’ll be talking about the Super Simple Solow model in a short webinar tomorrow (Tuesday March 26) at 1pm est. Register here.
Approaching Human-Level Forecasting with Language Models
Forecasting future events is important for policy and decision making. In this work, we study whether language models (LMs) can forecast at the level of competitive human forecasters. Towards this goal, we develop a retrieval-augmented LM system designed to automatically search for relevant information, generate forecasts, and aggregate predictions. To facilitate our study, we collect a large dataset of questions from competitive forecasting platforms. Under a test set published after the knowledge cut-offs of our LMs, we evaluate the end-to-end performance of our system against the aggregates of human forecasts. On average, the system nears the crowd aggregate of competitive forecasters, and in some settings surpasses it. Our work suggests that using LMs to forecast the future could provide accurate predictions at scale and help to inform institutional decision making.
That is from a new paper by Danny Halawi, Fred Zhang, Chen Yueh-Han, and Jacob Steinhardt. I hope you are all investing in that chrisma…
Comparing Large Language Models Against Lawyers
This paper presents a groundbreaking comparison between Large Language Models and traditional legal contract reviewers, Junior Lawyers and Legal Process Outsourcers. We dissect whether LLMs can outperform humans in accuracy, speed, and cost efficiency during contract review. Our empirical analysis benchmarks LLMs against a ground truth set by Senior Lawyers, uncovering that advanced models match or exceed human accuracy in determining legal issues. In speed, LLMs complete reviews in mere seconds, eclipsing the hours required by their human counterparts. Cost wise, LLMs operate at a fraction of the price, offering a staggering 99.97 percent reduction in cost over traditional methods. These results are not just statistics, they signal a seismic shift in legal practice. LLMs stand poised to disrupt the legal industry, enhancing accessibility and efficiency of legal services. Our research asserts that the era of LLM dominance in legal contract review is upon us, challenging the status quo and calling for a reimagined future of legal workflows.
That is from a new paper by Lauren Martin, Nick Whitehouse, Stephanie Yiu, Lizzie Catterson, and Rivindu Perera. Via Malinga.
Growth models and new goods
Growth models typically assume an inaccurate equivalence between the consumption of greater quantities of existing products (as an individual achieves by growing richer, all else equal) and the consumption of new products. As a result, they typically arbitrarily understate the welfare benefits of growth. They also arbitrarily overstate the extent which future growth will motivate a substitution from consumption to other goods. Finally, a more realistic model of new product introduction can be shown to alleviate the equity premium puzzle: steeply diminishing marginal utility in within-period consumption is compatible with a high saving rate because the marginal utility of consumption will be higher when new products are available.
That is a new paper from Philip Trammell, via Kris Gulati.
This also has implications for who should be subject to congestion pricing. I am currently in Chennai, which can be quite congested, most of all on the roads. Some kind of congestion fee (if it were possible to enforce) would be appropriate. But such a fee probably should not be levied on those who come to Chennai to consume new goods, or in other words visitors and outsiders. Those are also the people most likely to learn things from being in Chennai, and then to apply those learnings elsewhere. Beware of those who apply only a single microeconomic idea!
Using a Quantum Annealer to Solve a Real Business Cycle Model
From Jesús Fernández-Villaverde and Isaiah J. Hull a new paper:
NBER 31326: We introduce a novel approach to solving dynamic programming problems, such as those in many economic models, on a quantum annealer, a specialized device that performs combinatorial optimization. Quantum annealers attempt to solve an NP-hard problem by starting in a quantum superposition of all states and generating candidate global solutions in milliseconds, irrespective of problem size. Using existing quantum hardware, we achieve an order-of-magnitude speed-up in solving the real business cycle model over benchmarks in the literature. We also provide a detailed introduction to quantum annealing and discuss its potential use for more challenging economic problems.
Wikipedia offers more on quantum annealing:
Quantum annealing starts from a quantum-mechanical superposition of all possible states (candidate states) with equal weights. Then the system evolves following the time-dependent Schrödinger equation, a natural quantum-mechanical evolution of physical systems. The amplitudes of all candidate states keep changing, realizing a quantum parallelism, according to the time-dependent strength of the transverse field, which causes quantum tunneling between states or essentially tunneling through peaks. If the rate of change of the transverse field is slow enough, the system stays close to the ground state of the instantaneous Hamiltonian (also see adiabatic quantum computation).[6] If the rate of change of the transverse field is accelerated, the system may leave the ground state temporarily but produce a higher likelihood of concluding in the ground state of the final problem Hamiltonian, i.e., diabatic quantum computation.[7][8] The transverse field is finally switched off, and the system is expected to have reached the ground state of the classical Ising model that corresponds to the solution to the original optimization problem.
I would not have expected to see a paper like this for many years to come, even decades. I gather that solving the RBC model more quickly is a test case. I can see applications in knapsack problems and auction allocations.
Economics, Hayek, and Large Language Models
Here is my Hayek lecture at LSE, from earlier in this week:
Playing repeated games with Large Language Models
They are smart, but not ideal cooperators it seems, at least not without the proper prompts:
Large Language Models (LLMs) are transforming society and permeating into diverse applications. As a result, LLMs will frequently interact with us and other agents. It is, therefore, of great societal value to understand how LLMs behave in interactive social settings. Here, we propose to use behavioral game theory to study LLM’s cooperation and coordination behavior. To do so, we let different LLMs (GPT-3, GPT-3.5, and GPT-4) play finitely repeated games with each other and with other, human-like strategies. Our results show that LLMs generally perform well in such tasks and also uncover persistent behavioral signatures. In a large set of two players-two strategies games, we find that LLMs are particularly good at games where valuing their own self-interest pays off, like the iterated Prisoner’s Dilemma family. However, they behave sub-optimally in games that require coordination. We, therefore, further focus on two games from these distinct families. In the canonical iterated Prisoner’s Dilemma, we find that GPT-4 acts particularly unforgivingly, always defecting after another agent has defected only once. In the Battle of the Sexes, we find that GPT-4 cannot match the behavior of the simple convention to alternate between options. We verify that these behavioral signatures are stable across robustness checks. Finally, we show how GPT-4’s behavior can be modified by providing further information about the other player as well as by asking it to predict the other player’s actions before making a choice. These results enrich our understanding of LLM’s social behavior and pave the way for a behavioral game theory for machines.
Here is the full paper by Elif Akata, et.al.
*The Fall of the Turkish Model*
The author is Cihan Tuğal, and the subtitle is How the Arab Uprisings Brought Down Islamic Liberalism, though the book is more concretely a comparison across Egypt and Tunisia as well, with frequent remarks on Iran. Here is one excerpt:
This led to what Kevan Harris has called the ‘subcontractor state’: an economy which is neither centralized under a governmental authority not privatized and liberalized. The subcontractor state has decentralized its social and economic roles without liberalizing the economy or even straightforwardly privatizing the state-owned enterprises. As a result, the peculiar third sector of the Iranian economy has expanded in rather complicated and unpredictable ways. Rather than leading to liberalization privatization under revolutionary corporatism intensified and twisted the significance of organization such as the bonyads…Privatization under the populist-conservative Ahmedinejad exploited the ambiguities of the tripartite division of the economy…’Privatization’ entailed the sale of public assets not to private companies but to nongovernmental public enterprises (such as pension funds, the bonyads and military contractors).
This book is one useful background source for the current electoral process in Turkey.
Modeling the current NBA
The surprise, and the irony, is that the more good players there are, the more important the great ones have become. The proliferation of offensive threats has meant that defenses can’t train their attention all on one person; that means that there are better shots for the best players to take, and the best players have become even better at making them. They have more room to drive to the basket, where shots are hyper-efficient. They are more practiced and skilled at hitting long threes. They are better at drawing fouls and savvier about off-ball movement, picks, and screens. Most of all, perhaps, they can pass, and the threat of those passes makes them harder to defend. More than ever, offenses revolve around a single star—a phenomenon that many around the N.B.A. have taken to calling heliocentrism, a term that the Athletic writer Seth Partnow used in a 2019 column describing the Dallas Mavericks star Luka Dončić. Hero ball “didn’t go away,” Kirk Goldsberry, an ESPN analyst, told the podcast “ESPN Daily.” “It just went to M.I.T., got a degree in analytics, and rebranded as heliocentrism.”
South Park Commons — the collectives model for spurring innovation
From the NYT circa 2017:
…the [South Park] Commons aims to fill a hole in the tech landscape. Northern California is littered with incubators and accelerators, organizations like Y Combinator and Techstars that help small companies develop and grow. This is something different, a community you can join before you have founded a company or even when you have little interest in founding one.
The Commons is a bit like the hacker spaces that have long thrived in the Valley — places where coders and makers gather to build new software and hardware — but it moves beyond that familiar concept. Its founder, for one thing, is a female engineer turned entrepreneur turned executive.
From SPC itself:
SPC is a de-risking platform. The community addresses the social and intellectual components of risk—it provides a close-knit, high-talent group during the idea stage so members can reach founder-market fit before attempting product-market fit. The SPC Fund plays the more traditional role of de-risking finances: our recently-launched Community Grant works much like Emergent Ventures; the Founder Fellowship (we’re currently accepting applications) is designed to get would-be founders to take the plunge; and we participate in the broader VC ecosystem with some later-stage investments.
Reminds me of the Junto Club, not to mention the 18th century more broadly; SPC itself cites Junto as a model. Think of it as a technical community of people without full-time jobs, plus a venture fund. On the ground, technologists hang out with potential founders. Here is TechCrunch on SPC.
Which are other recent examples of successful “community” models for spurring innovation?
The Capacity for Moral Self-Correction in Large Language Models
We test the hypothesis that language models trained with reinforcement learning from human feedback (RLHF) have the capability to “morally self-correct” — to avoid producing harmful outputs — if instructed to do so. We find strong evidence in support of this hypothesis across three different experiments, each of which reveal different facets of moral self-correction. We find that the capability for moral self-correction emerges at 22B model parameters, and typically improves with increasing model size and RLHF training. We believe that at this level of scale, language models obtain two capabilities that they can use for moral self-correction: (1) they can follow instructions and (2) they can learn complex normative concepts of harm like stereotyping, bias, and discrimination. As such, they can follow instructions to avoid certain kinds of morally harmful outputs. We believe our results are cause for cautious optimism regarding the ability to train language models to abide by ethical principles.
By Deep Ganguli, et.al., many authors, here is the link. Via Aran.
If you worry about AGI risk, isn’t the potential for upside here far greater, under the assumption (which I would not accept) that AI can become super-powerful? Such an AI could create many more worlds and populate them with many more people, and so on. Is the chance of the evil demi-urge really so high?