More Like this

1. Decisions, decisions, decisions: plant roots detect and respond to complex environmental cues

   https://doi.org/10.1111/nph.16372  

   Callaway, Ragan M.; Li, Long (December 2019, New Phytologist)
2. Decisions Under Risk are Decisions Under Complexity

   Oprea, Ryan (November 2024, American Economic Review)

   This paper is forthcoming at the American Economic Review. 

   more » « less
3. Fast decisions reflect biases; slow decisions do not

   https://doi.org/10.1103/PhysRevE.110.024305  

   Linn, Samantha; Lawley, Sean_D; Karamched, Bhargav_R; Kilpatrick, Zachary_P; Josić, Krešimir (August 2024, Physical Review E)
4. Language-based decisions

   Bjorndahl, A.; Halpern, J. Y. (June 2021, Proceedings of Eighteenth Conference on Theoretical Aspects of Rationality and Knowledge (TARK))

   null (Ed.)

   In Savage's classic decision-theoretic framework, actions are formally defined as functions from states to outcomes. But where do the state space and outcome space come from? Expanding on recent work by Blume, Easley, and Halpern [2006], we consider a language-based framework in which actions are identified with (conditional) descriptions in a simple underlying language, while states and outcomes (along with probabilities and utilities) are constructed as part of a representation theorem. Our work expands the role of language from that of Blume, Easley, and Halpern by using it not only for the conditions that determine which actions are taken, but also the effects. More precisely, we take the set of actions to be built from those of the form do(phi), for formulas phi in the underlying language. This presents a problem: how do we interpret the result of do(phi) when phi is underspecified (i.e., compatible with multiple states)? We answer this using tools familiar from the semantics of counterfactuals; roughly speaking, do(phi) maps each state to the ``closest'' phi-state. This notion of ``closest'' is also something we construct as part of the representation theorem; in effect, then, we prove that (under appropriate assumptions) the agent is acting as if each underspecified action is first made definite and then evaluated (i.e., by maximizing expected utility). Of course, actions in the real world are often not presented in a fully precise manner, yet agents reason about and form preferences among them all the same. Our work brings the abstract tools of decision theory into closer contact with such real-world scenarios. 

   more » « less
5. Recovering Architectural Design Decisions

   Shahbazian, Arman; Lee, Youn Kyu; Le, Duc; Brun, Yuriy; Medvidovic, Nenad (April 2018, International Conference on Software Architecture)

   Designing and maintaining a software system’s architecture typically involve making numerous design decisions, each potentially affecting the system’s functional and nonfunctional properties. Understanding these design decisions can help inform future decisions and implementation choices and can avoid introducing regressions and architectural inefficiencies later. Unfortunately, design decisions are rarely well documented and are typically a lost artifact of the architecture creation and maintenance process. The loss of this information can thus hurt development. To address this shortcoming, we develop RecovAr, a technique for automatically recovering design decisions from the project’s readily available history artifacts, such as an issue tracker and version control repository. RecovAr uses state-ofthe- art architectural recovery techniques on a series of version control commits and maps those commits to issues to identify decisions that affect system architecture. While some decisions can still be lost through this process, our evaluation on Hadoop and Struts, two large open-source systems with over 8 years of development each and, on average, more than 1 million lines of code, shows that RecovAr has the recall of 75% and a precision of 77%. Our work formally defines architectural design decisions and develops an approach for tracing such decisions in project histories. Additionally, the work introduces methods to classify whether decisions are architectural and to map decisions to code elements. Finally, our work contributes a methodology engineers can follow to preserve design-decision knowledge in their projects. 

   more » « less