Consideration-set heuristics

Active Machine Learning for Consideration Heuristics

W develop and test an active-machine-learning method to select questions adaptively when consumers use heuristic decision rules. The method tailors priors to each consumer based on a “configurator.” Subsequent questions maximize information about the decision heuristics (minimize expected posterior entropy). To update posteriors after each question, we approximate the posterior with a variation...

Heuristic subset clustering for consideration set analysis

The term consideration set is used in marketing to refer to the set of items a customer thought about purchasing before making a choice. While consideration sets are not directly observable, finding common ones is useful for market segmentation and choice prediction. We approach the problem of inducing common consideration sets as a clustering problem. Our algorithm combines ideas from binary c...

New Greedy Heuristics For Set Cover and Set Packing

The Set Cover problem (SCP) and Set Packing problem (SPP) are standard NP-hard combinatorial optimization problems. Their decision problem versions are shown to be NP-Complete in Karp’s 1972 paper. We specify a rough guide to constructing approximation heuristics that may have widespread applications and apply it to devise greedy approximation algorithms for SCP and SPP, where the selection heu...

Heuristics for Maximum Clique and Independent Set

Experimenting with Heuristics for Answer Set Programming

Answer Set Programming (ASP) is a novel programming paradigm, which allows to solve problems in a simple and highly declarative way. The language of ASP (function-free disjunctive logic programming) is very expressive, and allows to represent even problems of high complexity (every problem in the complexity class P2 = NPNP). As for SAT solvers, the heuristic for the selection of the branching l...