38 research outputs found
Nie ufaj swojej intuicji : jak korzystać z danych, by osiągnąć sukces i cieszyć się życiem
No full textW "Nie ufaj swojej intuicji" Seth Stephen-Davidowitz, ekonomista, ex-analityk pracujący dla formy Google, pokazuje, jak wiele błędów popełniamy, kiedy starając się poprawić nasze życie, podejmujemy decyzje, odwołując się tylko do naszej intuicji. On sam analizuje dla nas setki tysięcy danych: wyniki z wyszukiwarek internetowych, dane z rejestrów podatkowych i portali randkowych, aby zaprezentować nam najskuteczniejsze sposoby randkowania, najlepsze miejsca do wychowywania dzieci i najbardziej efektywne strategie zrobienia kariery zawodowej i osiągnięcia osobistego szczęścia.In "Don't Trust Your Gut" Seth Stephen-Davidowitz, economist and former Google data scientist, reveals how wrong we really are when, trying to improve our own lives, we make decisions based solely on what our gut instinct tells us. He analyses for us hundreds of thousands data: Google searches, data from dating profiles and tax records to uncover the most successful strategies to get a date, the best places to raise children, and the most effective trajectories leading to success and personal happiness
SETH-Hardness of Coding Problems
No full textWe show that assuming the strong exponential-Time hypothesis (SETH), there are no non-Trivial algorithms for the nearest codeword problem (NCP), the minimum distance problem (MDP), or the nearest codeword problem with preprocessing (NCPP) on linear codes over any finite field. More precisely, we show that there are no NCP, MDP, or NCPP algorithms running in time q (1-ϵ)n for any constant ϵ>0 for codes with qn codewords. (In the case of NCPP, we assume non-uniform SETH.) We also show that there are no sub-exponential time algorithms for γ-Approximate versions of these problems for some constant γ > 1, under different versions of the exponential-Time hypothesis.NSF-BSF (Grant 1718161)NSF (Award 1350619
Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are. A Book Review.
No full textThis article reviews the book written by Seth Stephens-Davidowitz, titled Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are
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Essays Using Google Data
No full textI show three new ways to use Google search query data. First, I use Google search data to measure racism in the United States and its effect on Obama in the 2008 and 2012 presidential elections. Second, I use Google search data to predict turnout in different parts of the United States prior to an election. Third, I use Google search data to measure child maltreatment and how maltreatment is affected by economic downturns.Economic
Sex & (Self)Shaming: Exploring the Intersections of Kink, Sexual Desire and Ideology
No full textIn 2017, author Seth Stephens-Davidowitz explored the connection between sex and shame in his book Everybody Lies. Through the use of Google analytics, he revealed a staggering contradiction between sexual desire and personal ideology, finding that individual kinks and desires often contradict the lifestyle choices and behaviors that most impact public perception of identity. Interrogating the pornography habits of men and women, both in the United State and globally, Stephens-Davidowitz found that, overwhelmingly, “fantasy life isn’t always politically correct.”
If in its purest form sex is an expression of our deepest desires, feelings, and expressions of self, how and why does shame so often factor into the equation? Is guilt simply the natural manifestation of being a sexual person in a sex-negative, puritanical culture, or is the taboo of our fantasies to blame? What role does the pressure of conservative and progressive ideology play in not only the shame we feel, but the pleasure we gain from engaging in or fantasizing about sexual behaviors that seem to exist in direct opposition to our personal beliefs?
From “rape play” to “race play,” what do we do when we are most turned on by the things in life we battle against? Are we obligated to interrogate the ethical implications of our sexual desire and if so to what end and to whose benefit? Are sexual desires valid just because we feel them? Or do these desires stem from lived experiences in a culture that insists on the dominance and submission of certain groups of people? By policing our sexual interests, are we only oppressing ourselves? Through a queer theory and feminist lens, this presentation will explore the complex borderland between who we are publically, and who we are when no one is watching
Everybody Lies : Big Data dan Apa yang Diungkapkan Internet Tentang Siapa Kita Sesungguhnya
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Big Data, Small Mind
No full textData are getting bigger and they encroach ever more on individual and social decision making (Gigerenzer, 2022). This is for the good inasmuch data carry useful information. Information that is predictive, valid, and free from unwanted biases helps improve human welfare. Big data can reveal truths that challenge compelling intuitions or cherished beliefs. Given that our world is being flooded with petabytes of data, we can now ask what lessons it may offer to those who wish to make the best of their lives – and that would appear to be most of us. Seth Stephens-Davidowitz (SSD) responds to this quest in his provocatively titled book "Don't’ trust your gut: Using data to get what you really want in life.
Improved Hardness of BDD and SVP Under Gap-(S)ETH
No full textWe show improved fine-grained hardness of two key lattice problems in the _p norm: Bounded Distance Decoding to within an α factor of the minimum distance (BDD_{p, α}) and the (decisional) γ-approximate Shortest Vector Problem (GapSVP_{p,γ}), assuming variants of the Gap (Strong) Exponential Time Hypothesis (Gap-(S)ETH). Specifically, we show:
1) For all p ∈ [1, ∞), there is no 2^{o(n)}-time algorithm for BDD_{p, α} for any constant α > α_kn, where α_kn = 2^{-c_kn} < 0.98491 and c_kn is the ₂ kissing-number constant, unless non-uniform Gap-ETH is false.
2) For all p ∈ [1, ∞), there is no 2^{o(n)}-time algorithm for BDD_{p, α} for any constant α > α^‡_p, where α^‡_p is explicit and satisfies α^‡_p = 1 for 1 ≤ p ≤ 2, α^‡_p 2, and α^‡_p → 1/2 as p → ∞, unless randomized Gap-ETH is false.
3) For all p ∈ [1, ∞) ⧵ 2 ℤ and all C > 1, there is no 2^{n/C}-time algorithm for BDD_{p, α} for any constant α > α^†_{p, C}, where α^†_{p, C} is explicit and satisfies α^†_{p, C} → 1 as C → ∞ for any fixed p ∈ [1, ∞), unless non-uniform Gap-SETH is false.
4) For all p > p₀ ≈ 2.1397, p ∉ 2ℤ, and all C > C_p, there is no 2^{n/C}-time algorithm for GapSVP_{p, γ} for some constant γ > 1, where C_p > 1 is explicit and satisfies C_p → 1 as p → ∞, unless randomized Gap-SETH is false.
Our results for BDD_{p, α} improve and extend work by Aggarwal and Stephens-Davidowitz (STOC, 2018) and Bennett and Peikert (CCC, 2020). Specifically, the quantities α_kn and α^‡_p (respectively, α^†_{p,C}) significantly improve upon the corresponding quantity α_p^* (respectively, α_{p,C}^*) of Bennett and Peikert for small p (but arise from somewhat stronger assumptions). In particular, Item 1 improves the smallest value of α for which BDD_{p, α} is known to be exponentially hard in the Euclidean norm (p = 2) to an explicit constant α < 1 for the first time under a general-purpose complexity assumption. Items 1 and 3 crucially use the recent breakthrough result of Vlăduţ (Moscow Journal of Combinatorics and Number Theory, 2019), which showed an explicit exponential lower bound on the lattice kissing number. Finally, Item 4 answers a natural question left open by Aggarwal, Bennett, Golovnev, and Stephens-Davidowitz (SODA, 2021), which showed an analogous result for the Closest Vector Problem
