# Rule revision `kaal:entity:rule-revision` **Status.** derived This node is assembled mechanically from the 15 claims that carry the concept tag `rule-revision`. It is a roster of what the corpus says under this term. It is **not** an adjudicated definition: no single statement here has been ruled canonical, and no first-appearance call has been made. Read the claims and judge for yourself. ## Every claim under this term 15 claims across 8 works, 2012 to 2017. **2012** - [1998455-027](https://wulfkaal.github.io/claims/1998455-027) [mechanism/argued] -- Where jurisdictions are not compelled to agree on the same rule, some jurisdiction will try a different rule, and will do so more quickly, when changed economic circumstances make a different rule optimal. > diction will try a different rule, and will do so more quickly, than if all jurisdictions felt compelled to agree upon the same rule. Wulf A. Kaal, Initial Reflections on the Possible Application of Contingent Capital in Corporate Governance (2012). SSRN: https://ssrn.com/abstract=1998455 **2013** - [2267560-005](https://wulfkaal.github.io/claims/2267560-005) [failure/argued] *(failure mode)* -- Rulemaking conducted under conditions of incomplete information and bounded rationality produces suboptimal outcomes that require costly rule revisions, retractions, and additional rulemaking. > Rulemaking under conditions of incomplete information and bounded rationality can lead to suboptimal outcomes that may require rule revision and/or additional rulemaking. Wulf A. Kaal, Evolution of Law Dynamic Regulation in a New Institutional Economics Framework (2013). SSRN: https://ssrn.com/abstract=2267560 - [2267560-010](https://wulfkaal.github.io/claims/2267560-010) [failure/asserted] *(failure mode)* -- Even institutional arrangements that produce optimal governance solutions generate solutions that become suboptimal over time, necessitating rule revision, updating, and revocation. > Even if institutional arrangements produce optimal governance solutions, these solutions can become suboptimal over time, necessitating rule revision, updating, and revocation. Wulf A. Kaal, Evolution of Law Dynamic Regulation in a New Institutional Economics Framework (2013). SSRN: https://ssrn.com/abstract=2267560 - [2267560-020](https://wulfkaal.github.io/claims/2267560-020) [failure/argued] *(failure mode)* -- In the conventional NIE learning process, the requirements for rules and their adaptability to future states become clear only after stable and presumptively optimal rules have already emerged as suboptimal, so anticipation of future developments plays no role and learning is confined to learning from mistakes. > Anticipation of future developments does not play a role in this learning process. Wulf A. Kaal, Evolution of Law Dynamic Regulation in a New Institutional Economics Framework (2013). SSRN: https://ssrn.com/abstract=2267560 - [2267560-035](https://wulfkaal.github.io/claims/2267560-035) [failure/argued] *(failure mode)* -- The learning process in incomplete contract theory can be improved because experimentation with different rules, rule revision, and additional rulemaking create significant transaction costs, and postponing rulemaking until sufficient information is available may not always be possible or desirable. > Experimentation with different rules, rule revision, and additional rulemaking create significant transaction costs8 and postponing rulemaking until sufficient information is available may not always be possible or desirable. Wulf A. Kaal, Evolution of Law Dynamic Regulation in a New Institutional Economics Framework (2013). SSRN: https://ssrn.com/abstract=2267560 - [2273857-009](https://wulfkaal.github.io/claims/2273857-009) [mechanism/argued] -- Rulemakers discount or willingly accept unknown future contingencies and the inevitable need for later revision, amendment, and retraction, because they are pursuing certainty and predictability in the rules they enact. > To attain certainty and increase predictability, rulemakers discount or often willingly accept unknown future contingencies and the inevitable need for rule revision, amendments, and retractions. Wulf A. Kaal, Dynamic Regulation of the Financial Services Industry (2013). SSRN: https://ssrn.com/abstract=2273857 - [2273857-010](https://wulfkaal.github.io/claims/2273857-010) [failure/argued] *(failure mode)* -- A regulatory framework that relies exclusively on stable and presumptively optimal rules cannot adequately address future challenges, and the amendments, revisions, and retractions such a framework generates create substantial transaction costs and uncertainty. > A regulatory framework that relies exclusively on stable and presumptively optimal rules may not be able to adequately address future challenges. Amendments, revisions, and retractions of existing rules create substantial transaction costs and uncertainty. Wulf A. Kaal, Dynamic Regulation of the Financial Services Industry (2013). SSRN: https://ssrn.com/abstract=2273857 - [2348463-012](https://wulfkaal.github.io/claims/2348463-012) [failure/argued] *(failure mode)* -- Revised Rule 2019 clarifies some of the ambiguities of the old rule, but uncertainty and confusion about its application remain inevitable. > While Revised Rule 2019 clarifies some of the ambiguities under old Rule 2019, uncertainty and confusion still seem inevitable. Wulf A. Kaal, Hedge Funds’ Systemic Risk Disclosures in Bankruptcy (2013). SSRN: https://ssrn.com/abstract=2348463 **2014** - [kaal-2014-dynamicregulationviagove-009](https://wulfkaal.github.io/claims/kaal-2014-dynamicregulationviagove-009) [failure/evidenced] *(failure mode)* -- Experimentation with different rules under the current framework of stable rulemaking carries substantial costs of rule revision and enactment, and there is evidence that this framework does not protect against systemic shocks and financial crises. > The costs of rule revision, rule enactment, and exper- imentation in the current framework of stable rulemaking are substantial, especially because there is some evidence25 that the existing rulemaking framework does not protect against systemic shocks and financial crises. Kaal, Dynamic Regulation via Governmental Contracts (2014) - [kaal-2014-dynamicregulationviagove-013](https://wulfkaal.github.io/claims/kaal-2014-dynamicregulationviagove-013) [failure/argued] *(failure mode)* -- The existing framework of stable and presumptively optimal rules is self reinforcing: it perpetuates rulemaking processes that produce stable presumptively optimal rules and therefore keeps generating costly rule revision, updating, and revocation. > The existing framework for stable and presumptively optimal rules reinforces rulemaking processes that perpet- uate stable and presumptively optimal rules, necessitating costly rule revision, updating, and revocation. Kaal, Dynamic Regulation via Governmental Contracts (2014) - [kaal-2014-dynamicregulationviagove-014](https://wulfkaal.github.io/claims/kaal-2014-dynamicregulationviagove-014) [mechanism/argued] -- Feedback effects allow the necessary information to be acquired ex ante and necessary revisions to be anticipated before rules emerge as suboptimal, rather than ex post after failure has become apparent. > Rather than acquiring the necessary information ex-post after rules have emerged as suboptimal, feedback effects help increase the availability of rele- vant information for rulemaking ex-ante and anticipate necessary revisions before rules emerge as suboptimal Kaal, Dynamic Regulation via Governmental Contracts (2014) **2016** - [2740477-024](https://wulfkaal.github.io/claims/2740477-024) [mechanism/argued] *(failure mode)* -- Accepting suboptimal rules temporarily buys rule certainty and predictability but guarantees a later cycle of revision, amendment, and repeal, so rulemakers trade short term certainty for a costly correction process. > Temporary acceptance of suboptimal rules increases temporary rule certainty and predictability, but it also precipitates the inevitable need for rule revision, amendments, and repeals Wulf A. Kaal, Erik P.M. Vermeulen, Venture Capital as Dynamic Regulation of Disruptive Innovation (2016). SSRN: https://ssrn.com/abstract=2740477 - [2740477-025](https://wulfkaal.github.io/claims/2740477-025) [failure/argued] *(failure mode)* -- The current process of rule revisions, amendments, and repeals used to fix the inevitable shortcomings of stable rules is costly, time consuming, and in the authors' estimation cannot keep track of future innovations and the regulatory needs they create. > The current process of rule revisions, amendments, and repeals to address inevitable shortcomings of stable rules is costly, time-consuming, and cannot in our estimation keep track of future innovations and corresponding regulatory needs. Wulf A. Kaal, Erik P.M. Vermeulen, Venture Capital as Dynamic Regulation of Disruptive Innovation (2016). SSRN: https://ssrn.com/abstract=2740477 - [2808132-027](https://wulfkaal.github.io/claims/2808132-027) [failure/argued] *(failure mode)* -- The current process of rule revisions, amendments, and repeals used to correct the inevitable shortcomings of stable rules is costly and time-consuming, and in the authors' estimation it cannot keep track of future innovations and their corresponding regulatory needs. > important for rulemaking. The current process of rule revisions, amendments, and repeals to address inevitable shortcomings of stable rules is costly, time-consuming, and cannot, in our estimation, keep track of future innovations and corresponding regulatory needs. Wulf A. Kaal, Erik P.M. Vermeulen, How to Regulate Disruptive Innovation - From Facts to Data (2016). SSRN: https://ssrn.com/abstract=2808132 **2017** - [2957645-011](https://wulfkaal.github.io/claims/2957645-011) [mechanism/argued] -- Feedback effects change the timing of regulatory information: instead of acquiring necessary information only after rules have already proven suboptimal, they increase the availability of relevant information ex ante and support anticipation of necessary revisions. > Rather than acquiring necessary information after rules have emerged as suboptimal, feedback effects help increase the availability of relevant information for rulemaking ex ante and anticipate necessary revisions Kaal, Dynamic Regulation via Contingent Capital (2017). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2957645 ## Verify Every claim above resolves to a record carrying a verbatim source quote, the sha256 of the source PDF, and a preformatted citation. Nothing here asks to be taken on trust. curl -s https://wulfkaal.github.io/entities/rule-revision.md | sha256sum **Canonical form.** This markdown file is the canonical hashed representation of this entity node. Its sha256 is the content hash.