Problems of Monetary Management: The UK Experience

By C. Goodhart. 1984

Subfields: Economics & Operations Research

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Citations in the corpus

Scaling Laws for Reward Model Overoptimization (2022, background): “Goodhart’s Law in its modern formulation was first introduced in Hoskin (1996), with many of the key ideas introduced in prior works (Campbell, 1969; Goodhart, 1975).”
Concrete Problems in AI Safety (2016, background): “d on this measure, it might use more bleach than it needs, or simply pour bleach down the drain in order to give the appearance of success. In the economics literature this is known as Goodhart’s law [63]: \when a metric is used as a target, it ceases to be a good metric." Feedback Loops: Somet”
Unsolved Problems in ML Safety (2021, background): “Since proxies “will tend to collapse once pressure is placed upon” them by optimizers [64, 127, 169], proxies can often be gamed.”
Motivating the Rules of the Game for Adversarial Example Research (2018, background): “Adversarial robustness is an instance of the much broader phenomenon sometimes called Goodhart’s Law [82], where a metric ceases to be useful when it is used as a target for optimization.”
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities (2018, neutral): “Just as well-intentioned metrics in human society can become corrupted by direct pressure to optimize them (known as Campbell’s law [66] or Goodhart’s law [67]), digital evolution often acts to fulfill the letter of the law (i.”
This Thing Called Fairness (2019, background): “Economists have long noted that such strategic behavior can cause statistical relationships to break down and be arbitraged away through the phenomenon of Goodhart’s law [55].”
Harms from Increasingly Agentic Algorithmic Systems (2023, background): “As an instance of Goodhart’s law [Goodhart 1975], reward hacking is a common problem in ML systems that involve elements associated with increasing agency, in particular goal-directedness.”
Academic misconduct, misrepresentation and gaming: A reassessment (2019, background): “Goodhart’s Law, which postulates that any performance metric will attract gaming and rapidly lose its value as an indicator (Goodhart, 1984), suggests”
Black Swans, Lame Ducks, and the mystery of IPE's missing macroeconomy (2017, unknown): “As quoted in Goodhart (1981), p. 116.”
The natural selection of bad science (2016, background): “Output may be combined with impact—some researchers place emphasis on metrics such the h-index, defined as the largest number h such that an individual has h publications with at least h citations each (Hirsch, 2005).”
Quantilizers: A Safer Alternative to Maximizers for Limited Optimization (2016, background): “A grant from the Future of Life Institute helped fund this work.”
Defining and Characterizing Reward Hacking (2022, unknown): “Scholars have also criticized the assumption that human values can be encoded as rewards (Dobbe et al., 2021), and challenged the use of metrics more broadly (O’Neil, 2016; Thomas and Uminsky, 2022), citing Goodhart’s Law (Manheim and Garrabrant, 2018; Goodhart, 1975).”
Reward (Mis)design for Autonomous Driving (2021, unknown): “[42, 16].”
Embedded Agency (2019, background): “The misspeciﬁcation-amplifying e ﬀ ect is known as Goodhart’s law , named for Charles Goodhart’s (1975) observation: “Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.””
Optimal Inflation and the Identification of the Phillips Curve (2019, background): “Several authors over the years have also highlighted the general result that under an optimal control policy, the correlation between a policy target and policy instrument should be driven towards zero: including Worswick (1969); Peston (1972); Goodhart (1989) and (in the context of the Phillips cur”