Multi-unit Auction over a Social Network

Diffusion auction is an emerging business model where a seller aims to incentivise buyers in a social network to diffuse the auction information thereby attracting potential buyers. We focus on designing mechanisms for multi-unit diffusion auctions. Despite numerous attempts at this problem, existing mechanisms either fail to be incentive compatible (IC) or achieve only an unsatisfactory level of social welfare (SW). Here, we propose a novel graph exploration technique to realise multi-item diffusion auction. This technique ensures that potential competition among buyers stay ``localised'' so as to facilitate truthful bidding. Using this technique, we design multi-unit diffusion auction mechanisms MUDAN and MUDAN-$m$. Both mechanisms satisfy, among other properties, IC and $1/m$-weak efficiency. We also show that they achieve optimal social welfare for the class of rewardless diffusion auctions. While MUDAN addresses the bottleneck case when each buyer demands only a single item, MUDAN-$m$ handles the more general, multi-demand setting. We further demonstrate that these mechanisms achieve near-optimal social welfare through experiments

Connectivity in the presence of an opponent

The paper introduces two player connectivity games played on finite bipartite graphs. Algorithms that solve these connectivity games can be used as subroutines for solving M\"uller games. M\"uller games constitute a well established class of games in model checking and verification. In connectivity games, the objective of one of the players is to visit every node of the game graph infinitely often. The first contribution of this paper is our proof that solving connectivity games can be reduced to the incremental strongly connected component maintenance (ISCCM) problem, an important problem in graph algorithms and data structures. The second contribution is that we non-trivially adapt two known algorithms for the ISCCM problem to provide two efficient algorithms that solve the connectivity games problem. Finally, based on the techniques developed, we recast Horn's polynomial time algorithm that solves explicitly given M\"uller games and provide an alternative proof of its correctness. Our algorithms are more efficient than that of Horn's algorithm. Our solution for connectivity games is used as a subroutine in the algorithm

Characterizations of Network Auctions and Generalizations of VCG

With the growth of networks, promoting products through social networks has become an important problem. For auctions in social networks, items are needed to be sold to agents in a network, where each agent can bid and also diffuse the sale information to her neighbors. Thus, the agents' social relations are intervened with their bids in the auctions. In network auctions, the classical VCG mechanism fails to retain key properties. In order to better understand network auctions, in this paper, we characterize network auctions for the single-unit setting with respect to weak budget balance, individual rationality, incentive compatibility, efficiency, and other properties. For example, we present sufficient conditions for mechanisms to be efficient and (weakly) incentive compatible. With the help of these properties and new concepts such as rewards, participation rewards, and so on, we show how to design efficient mechanisms to satisfy incentive compatibility as much as possible, and incentive compatibility mechanisms to maximize the revenue. Our results provide insights into understanding auctions in social networks.Comment: To appear in ECAI 202

Differentially Private Diffusion Auction: The Single-unit Case

Diffusion auction refers to an emerging paradigm of online marketplace where an auctioneer utilises a social network to attract potential buyers. Diffusion auction poses significant privacy risks. From the auction outcome, it is possible to infer hidden, and potentially sensitive, preferences of buyers. To mitigate such risks, we initiate the study of differential privacy (DP) in diffusion auction mechanisms. DP is a well-established notion of privacy that protects a system against inference attacks. Achieving DP in diffusion auctions is non-trivial as the well-designed auction rules are required to incentivise the buyers to truthfully report their neighbourhood. We study the single-unit case and design two differentially private diffusion mechanisms (DPDMs): recursive DPDM and layered DPDM. We prove that these mechanisms guarantee differential privacy, incentive compatibility and individual rationality for both valuations and neighbourhood. We then empirically compare their performance on real and synthetic datasets

PENGARUH TINGKAT PENDIDIKAN DAN PENGEMBANGAN KARIR TERHADAP EFEKTIVITAS KERJA KARYAWAN DI PT CIOMAS ADISATWA RUMAH POTONG AYAM (RPA) UNIT MEDAN

Rumusan masalah dalam penelitian ini adalah Bagaimana  pengaruh Tingkat PendidikandanPengembanganKarirTerhadap EfektivitasKerjaKaryawan di PT CiomasAdisatwaRumahPotongAyam (RPA) Unit Medan baik secara parsial maupun simultan. Populasi dan sampel penelitian ini adalah seluruh karyawan di perusahaan tersebut yang berjumlah 100 orang. Teknik pengambilan sampel menggunakan total sampling. Teknik pengumpulan data yang digunakan adalah wawancara langsung dan angket. Hasil penelitian ini adalah secara parsial variabel Pendidikantidak  berpengaruh signifikan terhadap Efektivitaskerjakaryawan  dengan nilai thitung ttabel, (1,7851,984,  ), variabel PengembanganKarir   berpengaruh signifikan terhadap Efektivitaskerjakaryawan dengan nilai thitung ttabel, (4,5801,984). Secara Simultan variabel Tingkat pendidikan dan Pengembangan karir, berpengaruh positif dan signifikan terhadap Efektivitas Kerja karyawan dengan nilai Fhitung Ftabel,  (23,8033,09). Nilai R2 (RSquare) sebesar 0.329, yang berarti bahwa variabel Tingkat PendidikandanPengembanganKarirberpengaruh Positif dan signifikan terhadap EfektivitasKerjaKaryawan di PT CiomasAdisatwaRumahPotongAyam (RPA) Unit Medan  sebesar 32,90%, dan sisanya sebesar 67,10% lagi, dipengaruhi oleh faktor-faktor lain yang tidak diteliti

Learning Density-Based Correlated Equilibria for Markov Games

Correlated Equilibrium (CE) is a well-established solution concept that captures coordination among agents and enjoys good algorithmic properties. In real-world multi-agent systems, in addition to being in an equilibrium, agents' policies are often expected to meet requirements with respect to safety, and fairness. Such additional requirements can often be expressed in terms of the state density which measures the state-visitation frequencies during the course of a game. However, existing CE notions or CE-finding approaches cannot explicitly specify a CE with particular properties concerning state density; they do so implicitly by either modifying reward functions or using value functions as the selection criteria. The resulting CE may thus not fully fulfil the state-density requirements. In this paper, we propose Density-Based Correlated Equilibria (DBCE), a new notion of CE that explicitly takes state density as selection criterion. Concretely, we instantiate DBCE by specifying different state-density requirements motivated by real-world applications. To compute DBCE, we put forward the Density Based Correlated Policy Iteration algorithm for the underlying control problem. We perform experiments on various games where results demonstrate the advantage of our CE-finding approach over existing methods in scenarios with state-density concerns

Totalitarianism and geography: L.S. Berg and the defence of an academic discipline in the age of Stalin

In considering the complex relationship between science and politics, the article focuses upon the career of the eminent Russian scholar, Lev Semenovich Berg (1876–1950), one of the leading geographers of the Stalin period. Already before the Russian Revolution, Berg had developed a naturalistic notion of landscape geography which later appeared to contradict some aspects of Marxist–Leninist ideology. Based partly upon Berg's personal archive, the article discusses the effects of the 1917 revolution, the radical changes which Stalin's cultural revolution (from the late 1920s) brought upon Soviet science, and the attacks made upon Berg and his concept of landscape geography thereafter. The ways in which Berg managed to defend his notion of geography (sometimes in surprisingly bold ways) are considered. It is argued that geography's position under Stalin was different from that of certain other disciplines in that its ideological disputes may have been regarded as of little significance by the party leaders, certainly by comparison with its practical importance, thus providing a degree of ‘freedom’ for some geographers at least analogous to that which has been described by Weiner (1999. A little corner of freedom: Russian nature protection from Stalin to Gorbachev. Berkeley: University of California Press) for conservationists. It is concluded that Berg and others successfully upheld a concept of scientific integrity and limited autonomy even under Stalinism, and that, in an era of ‘Big Science’, no modernizing state could or can afford to emasculate these things entirely

Stochastic electrotransport selectively enhances the transport of highly electromobile molecules

Nondestructive chemical processing of porous samples such as fixed biological tissues typically relies on molecular diffusion. Diffusion into a porous structure is a slow process that significantly delays completion of chemical processing. Here, we present a novel electrokinetic method termed stochastic electrotransport for rapid nondestructive processing of porous samples. This method uses a rotational electric field to selectively disperse highly electromobile molecules throughout a porous sample without displacing the low-electromobility molecules that constitute the sample. Using computational models, we show that stochastic electrotransport can rapidly disperse electromobile molecules in a porous medium. We apply this method to completely clear mouse organs within 1–3 days and to stain them with nuclear dyes, proteins, and antibodies within 1 day. Our results demonstrate the potential of stochastic electrotransport to process large and dense tissue samples that were previously infeasible in time when relying on diffusion.Simons Foundation. Postdoctoral FellowshipLife Sciences Research FoundationBurroughs Wellcome Fund (Career Awards at the Scientific Interface)Searle Scholars ProgramMichael J. Fox Foundation for Parkinson's ResearchUnited States. Defense Advanced Research Projects AgencyJPB FoundationNational Institutes of Health (U.S.)National Institutes of Health (U.S.) (Grant 1-U01-NS090473-01