scholarly journals Delegating learning

2021 ◽  
Vol 16 (2) ◽  
pp. 571-603
Author(s):  
Juan F. Escobar ◽  
Qiaoxi Zhang
Keyword(s):  
Decision Making ◽  
Mechanism Design ◽  
Design Problem ◽  
Dynamic Mechanism ◽  
Organizational Decision Making ◽  
Trade Off ◽  
Dynamic Mechanism Design ◽  
Optimal Delegation ◽  
Over Time

Learning is crucial to organizational decision making but often needs to be delegated. We examine a dynamic delegation problem where a principal decides on a project with uncertain profitability. A biased agent, who is initially as uninformed as the principal, privately learns the profitability over time and communicates to the principal. We formulate learning delegation as a dynamic mechanism design problem and characterize the optimal delegation scheme. We show that private learning gives rise to the trade‐off between how much information to acquire and how promptly it is reflected in the decision. We discuss implications on learning delegation for distinct organizations.

From Monetary to Nonmonetary Mechanism Design via Artificial Currencies

2021 ◽  
Author(s):  
Artur Gorokh ◽  
Siddhartha Banerjee ◽  
Krishnamurthy Iyer
Keyword(s):  
Decision Making ◽  
Mechanism Design ◽  
Real World ◽  
Price Of Anarchy ◽  
Black Box ◽  
Trade Off ◽  
Two Agents ◽  
Over Time ◽  
Artificial Currency

Nonmonetary mechanisms for repeated allocation and decision making are gaining widespread use in many real-world settings. Our aim in this work is to study the performance and incentive properties of simple mechanisms based on artificial currencies in such settings. To this end, we make the following contributions: For a general allocation setting, we provide two black-box approaches to convert any one-shot monetary mechanism to a dynamic nonmonetary mechanism using an artificial currency that simultaneously guarantees vanishing gains from nontruthful reporting over time and vanishing losses in performance. The two mechanisms trade off between their applicability and their computational and informational requirements. Furthermore, for settings with two agents, we show that a particular artificial currency mechanism also results in a vanishing price of anarchy.

scholarly journals Evaluating Strategic Forecasters

2018 ◽  
Vol 108 (10) ◽  
pp. 3057-3103 ◽  
Author(s):  
Rahul Deb ◽  
Mallesh M. Pai ◽  
Maher Said
Keyword(s):  
Mechanism Design ◽  
Design Problem ◽  
Dynamic Mechanism ◽  
Future Event ◽  
High Quality ◽  
Dynamic Mechanism Design

Motivated by the question of how one should evaluate professional election forecasters, we study a novel dynamic mechanism design problem without transfers. A principal who wishes to hire only high-quality forecasters is faced with an agent of unknown quality. The agent privately observes signals about a publicly observable future event, and may strategically misrepresent information to inflate the principal’s perception of his quality. We show that the optimal deterministic mechanism is simple and easy to implement in practice: it evaluates a single, optimally timed prediction. We study the generality of this result and its robustness to randomization and noncommitment. (JEL C53, D72, D82)

scholarly journals The Biography of an Algorithm: Performing algorithmic technologies in organizations

2021 ◽  
Vol 2 (2) ◽  
pp. 263178772110046
Author(s):  
Vern L. Glaser ◽  
Neil Pollock ◽  
Luciana D’Adderio
Keyword(s):  
Decision Making ◽  
Organizational Capabilities ◽  
Negative Consequences ◽  
Organizational Decision Making ◽  
Computational Tools ◽  
Dynamic Perspective ◽  
Over Time

Algorithms are ubiquitous in modern organizations. Typically, researchers have viewed algorithms as self-contained computational tools that either magnify organizational capabilities or generate unintended negative consequences. To overcome this limited understanding of algorithms as stable entities, we propose two moves. The first entails building on a performative perspective to theorize algorithms as entangled, relational, emergent, and nested assemblages that use theories—and the sociomaterial networks they invoke—to automate decisions, enact roles and expertise, and perform calculations. The second move entails building on our dynamic perspective on algorithms to theorize how algorithms evolve as they move across contexts and over time. To this end, we introduce a biographical perspective on algorithms which traces their evolution by focusing on key “biographical moments.” We conclude by discussing how our performativity-inspired biographical perspective on algorithms can help management and organization scholars better understand organizational decision-making, the spread of technologies and their logics, and the dynamics of practices and routines.

The Aggregation–Learning Trade-off

2021 ◽  
Author(s):  
Henning Piezunka ◽  
Vikas A. Aggarwal ◽  
Hart E. Posen
Keyword(s):  
Decision Making ◽  
Crucial Role ◽  
Management Practice ◽  
Learning By Doing ◽  
Top Management Teams ◽  
Corporate Boards ◽  
Organizational Decision Making ◽  
Trade Off ◽  
Collective Wisdom ◽  
Alternative Decision

Organizational decision making that leverages the collective wisdom and knowledge of multiple individuals is ubiquitous in management practice, occurring in settings such as top management teams, corporate boards, and the teams and groups that pervade modern organizations. Decision-making structures employed by organizations shape the effectiveness of knowledge aggregation. We argue that decision-making structures play a second crucial role in that they shape the learning of individuals that participate in organizational decision making. In organizational decision making, individuals do not engage in learning by doing but, rather, in what we call learning by participating, which is distinct in that individuals learn by receiving feedback not on their own choices but, rather, on the choice made by the organization. We examine how learning by participating influences the efficacy of aggregation and learning across alternative decision-making structures and group sizes. Our central insight is that learning by participating leads to an aggregation–learning trade-off in which structures that are effective in aggregating information can be ineffective in fostering individual learning. We discuss implications for research on organizations in the areas of learning, microfoundations, teams, and crowds.

scholarly journals Dynamic Mechanism Design: An Introduction

2019 ◽  
Vol 57 (2) ◽  
pp. 235-274 ◽  
Author(s):  
Dirk Bergemann ◽  
Juuso Välimäki
Keyword(s):  
Mechanism Design ◽  
Dynamic Environment ◽  
Limited Liability ◽  
Dynamic Mechanism ◽  
Dynamic Mechanism Design ◽  
Dynamic Mechanisms ◽  
Sequential Screening ◽  
Open Questions ◽  
Recent Developments ◽  
Primary Focus

We provide an introduction to the recent developments of dynamic mechanism design, with a primary focus on the quasilinear case. First, we describe socially optimal (or efficient) dynamic mechanisms. These mechanisms extend the well-known Vickrey– Clark–Groves and D’Aspremont–Gérard–Varet mechanisms to a dynamic environment. Second, we discuss revenue optimal mechanisms. We cover models of sequential screening and revenue-maximizing auctions with dynamically changing bidder types. We also discuss models of information management where the mechanism designer can control (at least partially) the stochastic process governing the agents’ types. Third, we consider models with changing populations of agents over time. After discussing related models with risk-averse agents and limited liability, we conclude with a number of open questions and challenges that remain for the theory of dynamic mechanism design. ( JEL D44, D81, D82)

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