A takeoff on the idea that "real communism has never been tried, all they needed were computers".
From Real Life:
False Positivism
Why “planetary computation” and “data-driven governance” will not solve the world’s problems
During the pandemic, the everyday significance of modeling — data-driven representations of reality designed to inform planning — became inescapable. We viewed our plans, fears, and desires through the lens of statistical aggregates: Infection-rate graphs became representations not only of the virus’s spread but also of shattered plans, anxieties about lockdowns, concern for the fate of our communities.
But as epidemiological models became more influential, their implications were revealed as anything but absolute. One model, the Recidiviz Covid-19 Model for Incarceration, predicted high infection rates in prisons and consequently overburdened hospitals. While these predictions were used as the basis to release some prisoners early, the model has also been cited by those seeking to incorporate more data-driven surveillance technologies into prison management — a trend new AI startups like Blue Prism and Staqu are eager to get in on. Thus the same model supports both the call to downsize prisons and the demand to expand their operations, even as both can claim a focus on flattening the curve.
Impersonal, large-scale coordination by models can seem like an escape from subjective politics. It is rooted in a longstanding desire to streamline political thought
If insights from the same model can be used to justify wildly divergent interventions and political decisions, what should we make of the philosophical idea that data-driven modeling can liberate decision-making from politics? Advocates of algorithmic governance argue as though the “facts,” if enough are gathered and made efficacious, will disclose the “rational and equitable” response to existing conditions. And if existing models seem to offer ambiguous results, they might argue, it just means surveillance and technological intervention hasn’t been thorough enough.
But does a commitment to the facts really warrant ignoring concerns about individual privacy and agency? Many started to ask this question as new systems for contact tracing and penalizing violations of social distancing recommendations were proposed to fight the coronavirus. And does having enough “facts” really ensure that these systems can be considered unambiguously just? Even the crime-prediction platform PredPol, abandoned by the Los Angeles Police Department when critics and activists debunked its pseudo-scientific theories of criminal behavior and detailed its biases, jumped on the opportunity to rebrand itself as an epidemiological tool, a way to predict coronavirus spread and enforce targeted lockdowns. After all, it had already modeled crime as a “contagion-like process” — why not an actual contagion?
The ethics and effects of interventions depend not only on facts in themselves, but also on how facts are construed — and on what patterns of organization, existing or speculative, they are mobilized to justify. Yet the idea persists that data collection and fact finding should override concerns about surveillance, and not only in the most technocratic circles and policy think tanks. It also has defenders in the world of design theory and political philosophy. Benjamin Bratton, known for his theory of global geopolitics as an arrangement of computational technologies he calls “the Stack,” sees in data-driven modeling the only political rationality capable of responding to difficult social and environmental problems like pandemics and climate change. In his latest book, The Revenge of the Real: Politics for a Post-Pandemic World, he argues that expansive models — enabled by what he theorizes as “planetary-scale computation” — can transcend individualistic perspectives and politics and thereby inaugurate a more inclusive and objective regime of governance. Against a politically fragmented world of polarized opinions and subjective beliefs, these models, Bratton claims, would unite politics and logistics under a common representation of the world. In his view, this makes longstanding social concerns about personal privacy and freedom comparatively irrelevant and those who continue to raise them irrational....
....MUCH MORE
From October 2012's "Modelling vs. Science":
If an experiment is not reproducible it is not science.
If an hypothesis is not falsifiable it is not science.
Finally, our two guiding principles regarding models:
"All models are wrong, but some are useful"
