The show went well! It should be up here soon. Send Jeff a thank you note, a link to the book, and see if he’d like to take a look at the TACJ proposal

Add the following two papers to P33:

The Dictator Dilemma: The Distortion of Information Flow in Autocratic Regimes and Its Consequences

• Humans have been arguing about the benefits of dictatorial versus democratic regimes for millennia. Despite drastic differences between the dictatorships in the world, one of the key common features is the Dictator’s Dilemma as defined by Wintrobe [1]: a dictator will never know the true state of affairs in his country and is perpetually presented distorted information, thus having difficulties in making the right governing decisions. The dictator’s dilemma is essential to most autocratic regimes and is one of the key features in the literature on the subject. Yet, no quantitative theory of how the distortion of information develops from the initial state has been developed up to date. I present a model of the appearance and evolution of such information distortion, with subsequent degradation of control by the dictator. The model is based on the following fundamental and general premises: a) the dictator governs aiming to follow the desired trajectory of development based only on the information from the advisors; b) the deception from the advisors cannot decrease in time; and c) the deception change depends on the difficulties the country encounters. The model shows effective control in the short term (a few months to a year), followed by instability leading to the country’s gradual deterioration of the state over many years. I derive some universal parameters applicable to all dictators and show that advisors’ deception increases parallel with the decline of the control. In contrast, the dictator thinks the government is doing a reasonable, but not perfect, job. Finally, I present a match of our model to the historical data of grain production in the Soviet Union in 1928-1940.

The Tinpot and the Totalitarian: An Economic Theory of Dictatorship

• I use basic tools of economic theory to construct a simple model of the behavior of dictatorships. Two extreme cases are considered: a “tin-pot” dictatorship, in which the dictator wishes only to minimize the costs of remaining in power in order to collect the fruits of office (palaces, Mercedes-Benzes, Swiss bank accounts), and a “totalitarian” dictatorship, whose leader maximizes power over the population. I show that the two differ in their responses to economic change. For example, a decline in economic performance will lead a tin-pot regime to increase its repression of the population, whereas it will lead a totalitarian government to reduce repression. The model also shows why military dictatorships (a subspecies of tin-pots) tend to be short-lived and often voluntarily hand power over to a civilian regime; explains numerous features of totalitarian regimes; and suggests what policies will enable democratic regimes to deal with dictatorships effectively.

And maybe this one? The Ascendance Of Algorithmic Tyranny. Note the book it references – Seeing like a Platform: An Inquiry into the Condition
of Digital Modernity

• As today’s platforms become all-powerful, the metaphors we use to describe our digitally infused world exemplify a new, stealthier form of domination that is emerging.

Transformers are Graph Neural Networks

• We establish connections between the Transformer architecture, originally introduced for natural language processing, and Graph Neural Networks (GNNs) for representation learning on graphs. We show how Transformers can be viewed as message passing GNNs operating on fully connected graphs of tokens, where the self-attention mechanism capture the relative importance of all tokens w.r.t. each-other, and positional encodings provide hints about sequential ordering or structure. Thus, Transformers are expressive set processing networks that learn relationships among input elements without being constrained by apriori graphs. Despite this mathematical connection to GNNs, Transformers are implemented via dense matrix operations that are significantly more efficient on modern hardware than sparse message passing. This leads to the perspective that Transformers are GNNs currently winning the hardware lottery.

Tasks

• See what I need to apply for the SINTEF position
• Ping Daniel Thilo Schroeder about How Malicious AI Swarms Can Threaten Democracy
• Roll in Vanessa’s changes and sent her the next section. – done!
• Working on Carlos’ suggestion for funding
• Groceries
• Send an email to Jeff thanking him for having me on the show – done

SBIRs

• 9:00 standup – done
• Ping T about rates – nope, she’s away this week.