Incentivizing the Dynamic Workforce: Learning Contracts in the Gig-Economy

In principal-agent models, a principal offers a contract to an agent to perform a certain task. The agent exerts a level of effort that maximizes her utility. The principal is oblivious to the agent's chosen level of effort, and conditions her wage only on possible outcomes. In this work, we consider a model in which the principal is unaware of the agent's utility and action space. She sequentially offers contracts to identical agents, and observes the resulting outcomes. We present an algorithm for learning the optimal contract under mild assumptions. We bound the number of samples needed for the principal obtain a contract that is within $\epsilon$ of her optimal net profit for every $\epsilon>0$

Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems

Crowdsourcing markets have emerged as a popular platform for matching available workers with tasks to complete. The payment for a particular task is typically set by the task's requester, and may be adjusted based on the quality of the completed work, for example, through the use of "bonus" payments. In this paper, we study the requester's problem of dynamically adjusting quality-contingent payments for tasks. We consider a multi-round version of the well-known principal-agent model, whereby in each round a worker makes a strategic choice of the effort level which is not directly observable by the requester. In particular, our formulation significantly generalizes the budget-free online task pricing problems studied in prior work. We treat this problem as a multi-armed bandit problem, with each "arm" representing a potential contract. To cope with the large (and in fact, infinite) number of arms, we propose a new algorithm, AgnosticZooming, which discretizes the contract space into a finite number of regions, effectively treating each region as a single arm. This discretization is adaptively refined, so that more promising regions of the contract space are eventually discretized more finely. We analyze this algorithm, showing that it achieves regret sublinear in the time horizon and substantially improves over non-adaptive discretization (which is the only competing approach in the literature). Our results advance the state of art on several different topics: the theory of crowdsourcing markets, principal-agent problems, multi-armed bandits, and dynamic pricing.Comment: This is the full version of a paper in the ACM Conference on Economics and Computation (ACM-EC), 201

From supply chains to demand networks. Agents in retailing: the electrical bazaar

A paradigm shift is taking place in logistics. The focus is changing from operational effectiveness to adaptation. Supply Chains will develop into networks that will adapt to consumer demand in almost real time. Time to market, capacity of adaptation and enrichment of customer experience seem to be the key elements of this new paradigm. In this environment emerging technologies like RFID (Radio Frequency ID), Intelligent Products and the Internet, are triggering a reconsideration of methods, procedures and goals. We present a Multiagent System framework specialized in retail that addresses these changes with the use of rational agents and takes advantages of the new market opportunities. Like in an old bazaar, agents able to learn, cooperate, take advantage of gossip and distinguish between collaborators and competitors, have the ability to adapt, learn and react to a changing environment better than any other structure. Keywords: Supply Chains, Distributed Artificial Intelligence, Multiagent System.Postprint (published version

Flexible Decision Control in an Autonomous Trading Agent

An autonomous trading agent is a complex piece of software that must operate in a competitive economic environment and support a research agenda. We describe the structure of decision processes in the MinneTAC trading agent, focusing on the use of evaluators – configurable, composable modules for data analysis and prediction that are chained together at runtime to support agent decision-making. Through a set of examples, we show how this structure supports sales and procurement decisions, and how those decision processes can be modified in useful ways by changing evaluator configurations. To put this work in context, we also report on results of an informal survey of agent design approaches among the competitors in the Trading Agent Competition for Supply Chain Management (TAC SCM).autonomous trading agent;decision processes

Artificial Intelligence in the Formation of Contracts : an analysis of the adequacy of the Finnish contract law regime with regard to artificial intelligence

Lisääntynyt tekoälyn hyödyntäminen on herättänyt viime aikoina paljon keskustelua oikeustieteen saralla. Muun muassa itseohjautuviin autoihin liittyvät sopimuksen ulkopuoliset vastuukysymykset ja algoritmien hyödyntäminen oikeudellisessa päätöksenteossa ovat saaneet tässä keskustelussa paljon huomiota. Tekoälyä hyödynnetään hyvin monipuolisesti muillakin aloilla. Vähemmälle tarkastelulle ovat jääneet tekoälyn hyödyntämisen vaikutukset sopimusoikeuden näkökulmasta. Sähköisen kaupankäynnin lisääntyessä vuosituhannen vaihteessa katsottiin tarpeelliseksi päivittää sitä koskevaa sääntelyä tietyiltä osin. Tutkielman tavoitteena on selvittää, tuoko tekoälyn hyödyntäminen sellaisia uusia elementtejä digitaalisesti tehtyihin sopimuksiin, joiden vuoksi sopimusoikeutta koskevaa lainsäädäntöä olisi jälleen aihetta päivittää. Yksi keskeinen tätä hypoteesia puoltava havainto on tekoälyn valmius toimia hyvinkin autonomisesti. Tämä piirre vaikuttaa erottavan tekoälyyn perustuvat sopimukset muista digitaalisista sopimuksista. Ennen sopimusoikeudellisten vaikutusten arviointia tutkielmassa tarkastellaan, kuinka tekoälyä parhaillaan hyödynnetään sopimuksia tehtäessä. Havaitaan, että tekoälyä hyödynnetään jo nyt varsin monipuolisesti muun muassa sopimusehtojen tarkastamisessa, hinnoittelussa sekä ultranopeassa kaupankäynnissä rahoitusmarkkinoilla. Tutkielman pääasiallisena tavoitteena on arvioida sitä, ovatko olemassa olevat sopimusoikeuden säännöt riittäviä sääntelemään tekoälyn avulla solmittua sopimusta. Arviointi tehdään yllä mainittujen tekoälyn käyttökohteiden pohjalta. Arviointi on rajattu koskemaan Suomen oikeutta ja siinä tarkastellaan neljää sopimusoikeudellista kysymystä, jotka voivat olla ongelmallisia tekoälyn erityispiirteiden vuoksi: - Toteutuuko tahdonilmaisu riittävällä tavalla, kun sopimusta tehtäessä hyödynnetään tekoälyä? - Voidaanko erehdystä koskevia sääntöjä soveltaa, jos tekoäly toimii ennalta arvaamattomalla tavalla? - Miten vilpittömän mielen käsitettä tulisi soveltaa tekoälyn avulla tehtyjen sopimuksien yhteydessä? - Vaikuttaako tekoälyn hyödyntäminen sopimuksen tulkintaan? Tutkielmassa havaitaan, että tekoälyn rooli sopimuksen teossa vaihtelee paljon. Yksinkertaisimmillaan tekoälyä hyödynnetään apuvälineenä tietyn toiminnon automatisoinnissa. Tekoäly voi myös toimia vaativimmissa sovelluksissa autonomisessa roolissa siten, että älykäs tekoälyyn perustuva sovellus neuvottelee ja toimeenpanee sopimuksen itsenäisesti. Tekoälyn toimiessa autonomisessa roolissa sen erityispiirteiden havaittiin vaikuttavan eniten tässä tutkielmassa tarkasteltuihin sopimusoikeudellisiin kysymyksiin

Learn While You Earn: Two Approaches to Learning Auction Parameters in Take-it-or-leave-it Auctions

Much of the research in auction theory assumes that the auctioneer knows the distribution of participants ’ valuations with complete certainty. However, this is unrealistic. Thus, we analyse cases in which the auctioneer is uncertain about the valuation distributions; specifically, we consider a repeated auction setting in which the auctioneer can learn these distributions. Using take-it-or-leave-it auctions (Sandholm and Gilpin, 2006) as an exemplar auction format, we consider two auction design criteria. Firstly, an auctioneer could maximise expected revenue each time the auction is held. Secondly, an auctioneer could maximise the information gained in earlier auctions (as measured by the Kullback-Liebler divergence between its posterior and prior) to develop good estimates of the unknowns, which are later exploited to improve the revenue earned in the long-run. Simulation results comparing the two criteria indicate that setting offers to maximise revenue does not significantly detract from learning performance, but optimising offers for information gain substantially reduces expected revenue while not producing significantly better parameter estimates

Incentivizing the Dynamic Workforce: Learning Contracts in the Gig-Economy

In principal-agent models, a principal offers a contract to an agent to perform a certain task. The agent exerts a level of effort that maximizes her utility. The principal is oblivious to the agent's chosen level of effort, and conditions her wage only on possible outcomes. In this work, we consider a model in which the principal is unaware of the agent's utility and action space. She sequentially offers contracts to identical agents, and observes the resulting outcomes. We present an algorithm for learning the optimal contract under mild assumptions. We bound the number of samples needed for the principal obtain a contract that is within $\epsilon$ of her optimal net profit for every $\epsilon>0$