Information Design and Data Science 2019
2019-10-01..2019-10-03
Berkeley, United States
| title | author | length |
|---|---|---|
| Complementary Information and Learning Traps | Annie Liang | 44m |
Model uses linear Gaussian signals (K latent states, one payoff‑relevant omega, N signal types) and agents sequentially choose a single source to minimize… | ||
| Seeding with Costly Network Information | Dean Eckles | 50m |
Problem: maximize cascade size with limited, costly network measurements. Proposes spread queries (seed a node, observe its entire cascade) and edge queries… | ||
| Incrementality Bidding and Attribution | Randall Lewis | 47m |
Estimates causal ad value at auction scale by injecting randomization and using instrumental variables (two‑stage least squares / control function) to recover… | ||
| Obviousness Around the Clock | Sebastian Schweighofer-Kodritsch | 44m |
Replicates and extends Changu’s experiment: groups of four, 20 auctions, affiliated private values (group draw 10–110 plus individual 0–20), ~70 subjects per… | ||
| Credible Mechanism | Shengwu Li | 46m |
Defines auctioneer incentive compatibility (credible = auctioneer has no profitable deviation that looks legal to any bidder) and models ad-exchange-style… | ||
| The Content of Data: Backing out Preferences in Non-strategy Proof Mechanisms | Caterina Calsamilglia | 47m |
Presents a structural estimator that backs out preferences from submitted rank lists by modeling a mixed naive versus sophisticated population and solving… | ||
| Information Acquisition and Provision in School Choice: An Experimental Study | Yan Chen | 41m |
Lab experiment with 3 students and 3 schools compares Boston immediate acceptance versus Gale–Shapley deferred acceptance, elicits willingness to pay using a… | ||
| Dashboard Mechanisms for Online Marketplaces | Jason Hartline | 52m |
Proposes publishing per-agent bidding dashboards that estimate win probability for every bid and then inverting observed bids to recover values, allowing a… | ||
| Quality Selection in Two-Sided Markets: A Price Discrimination Approach | Ramesh Johari | 50m |
Reduction shows platform quality selection maps to a price-discrimination reduction: two stylized two-sided models (sellers choose quantity under platform-set… | ||
| Expectations-Based Loss Aversion May Help Explain... | Ori Heffetz | 49m |
Claim: expectations-based loss aversion explains why many people submit seemingly dominated rankings in strategy-proof matching (deferred acceptance / serial… | ||
| Lightning Talks | Shreyas Sekar, Krishnamurthy Iyer, Mariann Ollar, Francis Bloch, Ozan Candogan, Nikhil Garg, Michael Albert | 01h 17m |
Seven rapid projects present concrete models, algorithms and field tests: a Wayfair ranking algorithm uses a threshold scan to fix ranks in one pass, proves a… | ||
| Diversity in Matching Markets | John Dickerson | 50m |
Introduces an online bipartite matching method that maximizes a monotone submodular objective via the multilinear extension and two simple online policies: a… | ||
| Crowdsourcing Information in Informal Supply Chains | Irene Lo | 46m |
A deployed field platform called Pimping Pen app crowdsources mill prices and transaction logs from transportation middlemen in Indonesian palm fruit supply… | ||
| Identification and Estimation of Dynamic Structural Models with Unobserved Choices | Yi Xin | 44m |
Claims that dynamic discrete-choice models remain identifiable when actions are unobserved choices by using time variation in a finite-horizon setting as an… | ||
| You Can Lead a Horse to Water: Consumer Search with Spatial Learning | Charles Hodgson | 39m |
Charles Hodgson (Yale University)
https://simons.berkeley.edu/talks/you-can-lead-horse-water
Information Design and Data Science | ||
| nothing matches | ||