Dynamic project selection

Nikandrova, Arina; Pancs, Romans

doi:10.3982/te2379

Cited by 23 publications

(9 citation statements)

References 27 publications

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“…Our bandit construction applies because the stopping thresholds are restricted to be independent of the DM's expectations about the other items; if both the attention strategy and the stopping region are optimized as in Nikandrova and Pancs (2018) and Ke and Villas‐Boas (2019), then there need not exist an optimal Gittins index strategy and IIA is not guaranteed if there are more than two items. (As noted above, with only two items, every strategy trivially satisfies IIA.…”

Section: Manipulation Of One‐shot Choicementioning

confidence: 99%

“…Our model builds on a long tradition in statistics and economic theory originating with Wald (1945), who proposed a theory of optimal sequential learning about a single binary state. A growing literature studies optimal sequential learning about several options when attention must focus on one item at a time (Mandelbaum, Shepp, and Vanderbei (1990), Ke, Shen, and Villas‐Boas (2016), Ke and Villas‐Boas (2019), Nikandrova and Pancs (2018), Austen‐Smith and Martinelli (2018)). The structure of the optimal learning strategy varies depending on the costs and information structure.…”

Section: Introductionmentioning

confidence: 99%

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Gossner

Steiner

Stewart

2021

ECTA

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We study the impact of manipulating the attention of a decision‐maker who learns sequentially about a number of items before making a choice. Under natural assumptions on the decision‐maker's strategy, directing attention toward one item increases its likelihood of being chosen regardless of its value. This result applies when the decision‐maker can reject all items in favor of an outside option with known value; if no outside option is available, the direction of the effect of manipulation depends on the value of the item. A similar result applies to manipulation of choices in bandit problems.

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Section: Manipulation Of One‐shot Choicementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Attention Please!

Gossner

Steiner

Stewart

2021

ECTA

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“…This means that if the DM is unlucky and waits for a longer time before receiving breakthrough news, the accuracy of her action will suffer. 37 See the discussion of Nikandrova and Pancs (2018) and Mayskaya (2016) in the Introduction. 38 As far as we know, even in the Wald stopping problem, tractable characterizations are not available for more than two states (Peskir and Shiryaev, 2006).…”

Section: Non-conclusive Evidencementioning

confidence: 99%

Optimal Dynamic Allocation of Attention

Che

Mierendorff

2019

American Economic Review

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We consider a decision maker (DM) who, before taking an action, seeks information by allocating her limited attention dynamically over different news sources that are biased toward alternative actions. Endogenous choice of information generates rich dynamics: The chosen news source either reinforces or weakens the prior, shaping subsequent attention choices, belief updating, and the final action. The DM adopts a learning strategy biased toward the current belief when the belief is extreme and against that belief when it is moderate. Applied to consumption of news media, observed behavior exhibits an "echo-chamber" effect for partisan voters and a novel "anti echo-chamber" effect for moderates. DDMs, which lead to a continuous belief process, are more suitable for the problem of learning the properties of a data-generating process from a sequence of samples, as in clinical trials, or for the analysis of statistical information. Poisson models, which lead to discontinuous updating, are useful to model the discovery of individual pieces of information that are very informative, as is common in the political 10 Since the belief is a martingale, we have λαDividing by dt and letting dt → 0 yields (2.1).11 These examples are similar in spirit to Suen (2004) and Burke (2008), except that information is not costly in these models, which leads to a different characterization. In particular, dynamics has no significant effect in these models, in contrast to the point made here.

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“…In our setting, it is a priori unknown which approach is efficient, and the agent balances learning and managing time pressure. Callander (2011), Garfagnini andStrulovici (2016), Francetich (2018), Nikandrova and Pancs (2018), and Che and Mierendorff (2019) study the problem of dynamically distributing effort across several projects. However, none of them addresses the risk-time tradeoff.…”

Section: Introductionmentioning

confidence: 99%

“…By construction, neither reproduces the switching dynamics we obtain. Nikandrova and Pancs (2018) model an agent who irreversibly selects between two alternatives. The agent uses experimentation to learn about her options beforehand.…”

Section: Introductionmentioning

confidence: 99%

On Risk and Time Pressure: When to Think and When to Do

Carnehl,

Schneider

2021

Preprint

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We study the tradeoff between fundamental risk and time. A time-constrained agent has to solve a problem. She dynamically allocates effort between implementing a risky initial idea and exploring alternatives. Discovering an alternative implies progress that has to be converted to a solution. As time runs out, the chances of converting it in time shrink. We show that the agent may return to the initial idea after having left it in the past to explore alternatives. Our model helps explain so-called false starts. The agent takes risks early on to quickly arrive at a solution, sacrificing the prospects of alternatives.

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Dynamic project selection

Cited by 23 publications

References 27 publications

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Optimal Dynamic Allocation of Attention

On Risk and Time Pressure: When to Think and When to Do

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