Anytime Heuristic for Weighted Matching Through Altruism-Inspired Behavior

We present a novel anytime heuristic (ALMA), inspired by the human principle of altruism, for solving the assignment problem. ALMA is decentralized, completely uncoupled, and requires no communication between the participants. We prove an upper bound on the convergence speed that is polynomial in the desired number of resources and competing agents per resource; crucially, in the realistic case where the aforementioned quantities are bounded independently of the total number of agents/resources, the convergence time remains constant as the total problem size increases. We have evaluated ALMA under three test cases: (i) an anti-coordination scenario where agents with similar preferences compete over the same set of actions, (ii) a resource allocation scenario in an urban environment, under a constant-time constraint, and finally, (iii) an on-line matching scenario using real passenger-taxi data. In all of the cases, ALMA was able to reach high social welfare, while being orders of magnitude faster than the centralized, optimal algorithm. The latter allows our algorithm to scale to realistic scenarios with hundreds of thousands of agents, e.g., vehicle coordination in urban environments

Courtesy as a Means to Coordinate

We investigate the problem of multi-agent coordination under rationality constraints. Specifically, role allocation, task assignment, resource allocation, etc. Inspired by human behavior, we propose a framework (CA^3NONY) that enables fast convergence to efficient and fair allocations based on a simple convention of courtesy. We prove that following such convention induces a strategy which constitutes an $\epsilon$-subgame-perfect equilibrium of the repeated allocation game with discounting. Simulation results highlight the effectiveness of CA^3NONY as compared to state-of-the-art bandit algorithms, since it achieves more than two orders of magnitude faster convergence, higher efficiency, fairness, and average payoff.Comment: Accepted at AAMAS 2019 (International Conference on Autonomous Agents and Multiagent Systems

Scalable Learning of Bayesian Networks Using Feedback Arc Set-Based Heuristics

Bayesianske nettverk er en viktig klasse av probabilistiske grafiske modeller. De består av en struktur (en rettet asyklisk graf) som beskriver betingede uavhengighet mellom stokastiske variabler og deres parametere (lokale sannsynlighetsfordelinger). Med andre ord er Bayesianske nettverk generative modeller som beskriver simultanfordelingene på en kompakt form. Den største utfordringen med å lære et Bayesiansk nettverk skyldes selve strukturen, og på grunn av den kombinatoriske karakteren til asyklisitetsegenskapen er det ingen overraskelse at strukturlæringsproblemet generelt er NP-hardt. Det eksisterer algoritmer som løser dette problemet eksakt: dynamisk programmering og heltalls lineær programmering er de viktigste kandidatene når man ønsker å finne strukturen til små til mellomstore Bayesianske nettverk fra data. På den annen side er heuristikk som bakkeklatringsvarianter ofte brukt når man forsøker å lære strukturen til større nettverk med tusenvis av variabler, selv om disse heuristikkene vanligvis ikke har teoretiske garantier og ytelsen i praksis kan bli uforutsigbar når man arbeider med storskala læring. Denne oppgaven tar for seg utvikling av skalerbare metoder som takler det strukturlæringsproblemet av Bayesianske nettverk, samtidig som det forsøkes å opprettholde et nivå av teoretisk kontroll. Dette ble oppnådd ved bruk av relaterte kombinatoriske problemer, nemlig det maksimale asykliske subgrafproblemet (maximum acyclic subgraph) og det duale problemet (feedback arc set). Selv om disse problemene er NP-harde i seg selv, er de betydelig mer håndterbare i praksis. Denne oppgaven utforsker måter å kartlegge Bayesiansk nettverksstrukturlæring til maksimale asykliske subgrafforekomster og trekke ut omtrentlige løsninger for det første problemet, basert på løsninger oppnådd for det andre. Vår forskning tyder på at selv om økt skalerbarhet kan oppnås på denne måten, er det adskillig mer utfordrende å opprettholde den teoretisk forståelsen med denne tilnærmingen. Videre fant vi ut at å lære strukturen til Bayesianske nettverk basert på maksimal asyklisk subgraf kanskje ikke er den beste metoden generelt, men vi identifiserte en kontekst - lineære strukturelle ligningsmodeller - der vi eksperimentelt kunne validere fordelene med denne tilnærmingen, som fører til rask og skalerbar identifisering av strukturen og med mulighet til å lære komplekse strukturer på en måte som er konkurransedyktig med moderne metoder.Bayesian networks form an important class of probabilistic graphical models. They consist of a structure (a directed acyclic graph) expressing conditional independencies among random variables, as well as parameters (local probability distributions). As such, Bayesian networks are generative models encoding joint probability distributions in a compact form. The main difficulty in learning a Bayesian network comes from the structure itself, owing to the combinatorial nature of the acyclicity property; it is well known and does not come as a surprise that the structure learning problem is NP-hard in general. Exact algorithms solving this problem exist: dynamic programming and integer linear programming are prime contenders when one seeks to recover the structure of small-to-medium sized Bayesian networks from data. On the other hand, heuristics such as hill climbing variants are commonly used when attempting to approximately learn the structure of larger networks with thousands of variables, although these heuristics typically lack theoretical guarantees and their performance in practice may become unreliable when dealing with large scale learning. This thesis is concerned with the development of scalable methods tackling the Bayesian network structure learning problem, while attempting to maintain a level of theoretical control. This was achieved via the use of related combinatorial problems, namely the maximum acyclic subgraph problem and its dual problem the minimum feedback arc set problem. Although these problems are NP-hard themselves, they exhibit significantly better tractability in practice. This thesis explores ways to map Bayesian network structure learning into maximum acyclic subgraph instances and extract approximate solutions for the first problem, based on the solutions obtained for the second. Our research suggests that although increased scalability can be achieved this way, maintaining theoretical understanding based on this approach is much more challenging. Furthermore, we found that learning the structure of Bayesian networks based on maximum acyclic subgraph/minimum feedback arc set may not be the go-to method in general, but we identified a setting - linear structural equation models - in which we could experimentally validate the benefits of this approach, leading to fast and scalable structure recovery with the ability to learn complex structures in a competitive way compared to state-of-the-art baselines.Doktorgradsavhandlin

Cybersecurity of Digital Service Chains

This open access book presents the main scientific results from the H2020 GUARD project. The GUARD project aims at filling the current technological gap between software management paradigms and cybersecurity models, the latter still lacking orchestration and agility to effectively address the dynamicity of the former. This book provides a comprehensive review of the main concepts, architectures, algorithms, and non-technical aspects developed during three years of investigation; the description of the Smart Mobility use case developed at the end of the project gives a practical example of how the GUARD platform and related technologies can be deployed in practical scenarios. We expect the book to be interesting for the broad group of researchers, engineers, and professionals daily experiencing the inadequacy of outdated cybersecurity models for modern computing environments and cyber-physical systems

Cybersecurity of Digital Service Chains

This open access book presents the main scientific results from the H2020 GUARD project. The GUARD project aims at filling the current technological gap between software management paradigms and cybersecurity models, the latter still lacking orchestration and agility to effectively address the dynamicity of the former. This book provides a comprehensive review of the main concepts, architectures, algorithms, and non-technical aspects developed during three years of investigation; the description of the Smart Mobility use case developed at the end of the project gives a practical example of how the GUARD platform and related technologies can be deployed in practical scenarios. We expect the book to be interesting for the broad group of researchers, engineers, and professionals daily experiencing the inadequacy of outdated cybersecurity models for modern computing environments and cyber-physical systems

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Digital user's decision journey

The landscape of the Internet is continually evolving. This creates huge opportunities for different industries to optimize vital channels online, resulting in various-forms of new Internet services. As a result, digital users are interacting with many digital systems and they are exhibiting dynamic behaviors. Their shopping behaviors are drastically different today than it used to be, with offline and online shopping interacting with each other. They have many channels to access online media but their consumption patterns on different channels are quite different. They do philanthropy online to help others but their heterogeneous motivations and different fundraising campaigns leads to distinct path-to-contribution. Understanding the digital user’s decision making process behind their dynamic behaviors is critical as they interact with various digital systems for the firms to improve user experience and improve their bottom line. In this thesis, I study digital users’ decision journeys and the corresponding digital technology firms’ strategies using inter-disciplinary approaches that combine econometrics, economic structural modeling and machine learning. The uncovered decision journey not only offer empirical managerial insights but also provide guideline for introducing intervention to better serve digital users

Three Empirical Studies in Market Design

Market design is the development of mechanisms that improve market efficiency and build on an understanding of the interaction between human behavior and market rules. The first chapter considers the sale of a charitable membership where the charity poses the market design question of how to price these memberships to capture the maximum value from donors' altruism. Using an online natural field experiment with over 700,000 subjects, this chapter tests theory on price discounts and shows large differences in donation behavior between donors who have previously given money and/or volunteered. For example, framing the charity's membership price as a discount increases response rates and decreases conditional contributions from former volunteers, but not from past money donors. This chapter thereby demonstrates the importance of conditioning fundraising strategies on the specifics of past donation dimensions. The second chapter examines an auction used to solve the assignment and price determination problems where price depends on the propensity to own or farm the land, a non-market good. This chapter studies bidder behavior in a reverse auction where landowners compete to sell and retire the right to develop their farmland. A reduced form bidding model is used to estimate the role of bidder competition, winner's curse correction, and the underlying distribution of private values. The chapter concludes that the auction enrolled as much as 3,000 acres (12 percent) more than a take-it-or-leave-it offer (i.e., non-auction program) would have enrolled for the same budgetary cost. Finally, the third chapter considers the online advertising word auction. The pricing determination and assignment problem must occur for over 2,000 consumer searches each second. Theory is developed where asymmetric advertisers compete and an advertiser-optimal equilibrium bidding strategy is presented that is robust to this asymmetry. Within this rich strategy space, it is shown that advertiser subsidization can be revenue increasing for the search engine. Using a novel dataset of more than 4,500 keyword bids by three firms on four search engines, a simulation of the auction environment illustrates that bidder subsidization is indeed revenue positive and can be improved upon by imposing bid caps or fixed bids on the subsidized bidder