Working Papers

Seria wydawnicza WNE WORKING PAPERS prowadzona jest przez WNE UW od 2008 r. i jak dotąd opublikowano w niej ponad 400 prac.

Seria WNE Working Papers służy szybkiemu upowszechnianiu wyników badań prowadzonych na Wydziale Nauk Ekonomicznych UW. Publikowane tu teksty mają charakter wersji roboczych (pre-printów): mogą być uzupełniane lub poprawiane w miarę postępu prac nad tekstem i procesu wydawniczego. Prezentujemy aktualną wersję udostępnioną przez autorów, opatrzoną datą oraz identyfikatorami DOI/ISSN – co ułatwia jej cytowanie i śledzenie kolejnych iteracji. Każdy dokument zachowuje pełne prawa autorskie swoich twórców, a w przypadku późniejszych zmian, autorzy mogą w dowolnym momencie zamieścić zaktualizowany plik lub wskazać miejsce ostatecznego wydania.

Do serii WORKING PAPERS przyjmowane są artykuły pracowników naukowych WNE UW oraz publikacje z konferencji organizowanych na WNE UW. Artykuły powinny dotyczyć ekonomii, mieć charakter oryginalnych prac badawczych, nie być wcześniej publikowane. Przyjmowane są wyłącznie teksty w języku angielskim.

Prace prosimy przesyłać e-mailem na adres: ?3oK|q.*%7spM=wx-mQL_^bufg&]#[3}b3f`puc'YcAUogmvGDhDGkoXp

Prosimy przesyłać dwa pliki:

(1) główny tekst bez podania tytułu artykułu oraz autorów (plik DOC/DOCX) oraz

(2) stronę tytułową zawierającą: tytuł opracowania, autorów oraz ich afiliację (plik DOC/DOCX). 

Przed przesłaniem tekstu prosimy zapoznać się z szczegółowymi wymogami edycyjnymi.

Redaktorem wydawniczym serii Working Papers jest dr Maciej Bukowski.

Ilość wyników 6

WP(8/2026)502. Keynes vs. Kolmogorov: Two Axiomatics of Probability

Autorzy: Ryłow Jakub
The paper examines the logical theory of probability formulated by John Maynard Keynes in A Treatise on Probability (1921) as an axiomatic project competing with the measure-theoretic approach codified by Andrei Kolmogorov in Grundbegriffe der Wahrsc…

WP502. Keynes vs. Kolmogorov: Two Axiomatics of Probability

The paper examines the logical theory of probability formulated by John Maynard Keynes in A Treatise on Probability (1921) as an axiomatic project competing with the measure-theoretic approach codified by Andrei Kolmogorov in Grundbegriffe der Wahrscheinlichkeitsrechnung (1933). Beyond a synthetic comparison of both systems and their contemporary successors, the paper makes two original contributions. First, we identify and formally prove the Boolean presupposition of Ramsey's Dutch book argument: the argument establishes only a conditional result — if the event domain is a Boolean algebra, then coherence requires classical probability — whose antecedent is precisely what Keynes's partial order postulate denies. Ramsey therefore did not refute Keynesian logicism unconditionally; he refuted it within a domain whose universality Keynes contested. This formalises, using the Wroński–Godziszewski (2016) theorem, a gap in the standard history of the debate that has gone unnamed in the literature. Second, we propose the imprecision index of a credal set as the first candidate formal analogue of Keynes's weight of evidence within the established framework of imprecise probabilities.


DOI: https://doi.org/10.33138/2957-0506.2026.8.502
Ryłow Jakub
logical probability measure theory weight of evidence imprecise probabilities Bayesianism

WP(10/2022)386. Subjective Expectations and Uncertainty

Autorzy: Kocięcki Andrzej, Łyziak Tomasz, Stanisławska Ewa
Analysis of macroeconomic expectations of private sector agents reveals not only the path of expected macroeconomic developments and the mechanism of expectation formation, but also the degree of uncertainty faced by economic agents. Relying on this …

WP386. Subjective Expectations and Uncertainty

Analysis of macroeconomic expectations of private sector agents reveals not only the path of expected macroeconomic developments and the mechanism of expectation formation, but also the degree of uncertainty faced by economic agents. Relying on this observation the aim of this paper is twofold. First, we lay down the formal theory of subjective expectations and derive the subjective assessment hypothesis, which defines the optimal forecast of an individual under a specific loss function. Keeping the mathematical rigor, the proposed theory attempts to reflect the process of expectation formation that is consistent with well known behavioral features. Second, we apply the subjective assessment hypothesis to pin down the notion of uncertainty. We propose a novel uncertainty index, retrieved from forecast revisions of professional forecasters using an empirical model within the Bayesian approach. The two versions of this index derived for the US—based either on GDP growth or on inflation forecasts—describe different kinds of macroeconomic uncertainty. Their shocks act similarly as demand shocks, pushing economic activity and inflation down.


Kocięcki Andrzej, Łyziak Tomasz, Stanisławska Ewa
Subjective expectations Uncertainty Stochastic volatility Identification Bayesian approach Survey of Professional Forecasters

WP(8/2020)314. Novel multilayer stacking framework with weighted ensemble approach for multiclass credit scoring problem application

Autorzy: Stelmach Marek, Chlebus Marcin
Stacked ensembles approaches have been recently gaining importance in complex predictive problems where extraordinary performance is desirable. In this paper we develop a multilayer stacking framework and apply it to a large dataset related to credit…

WP314. Novel multilayer stacking framework with weighted ensemble approach for multiclass credit scoring problem application

Stacked ensembles approaches have been recently gaining importance in complex predictive problems where extraordinary performance is desirable. In this paper we develop a multilayer stacking framework and apply it to a large dataset related to credit scoring with multiple, imbalanced classes. Diverse base estimators (among others, bagged and boosted tree algorithms, regularized logistic regression, neural networks, Naive Bayes classifier) are examined and we propose three meta learners to be finally combined into a novel, weighted ensemble. To prevent bias in meta features construction, we introduce a nested cross-validation schema into the architecture, while weighted log loss evaluation metric is used to overcome training bias towards the majority class. Additional emphasis is placed on a proper data preprocessing steps and Bayesian optimization for hyperparameter tuning to ensure that the solution do not overfits. Our study indicates better stacking results compared to all individual base classifiers, yet we stress the importance of an assessment whether the improvement compensates increased computational time and design complexity. Furthermore, conducted analysis shows extremely good performance among bagged and boosted trees, both in base and meta learning phase. We conclude with a thesis that a weighted meta ensemble with regularization properties reveals the least overfitting tendencies.


Stelmach Marek, Chlebus Marcin
stacked ensembles nested cross-validation Bayesian optimization multiclass problem imbalanced classes

WP(14/2015)162. What is the Causal Effect of Knowledge on Preferences?

Autorzy: LaRiviere Jacob, Czajkowski Mikołaj, Hanley Nick, Simpson Katherine
We use a novel field experiment which jointly tests two implicit assumptions of updating models in a joint framework: that new information leads to new knowledge and that new knowledge can affect economic decisions. In the experiment, we elicit subje…

WP162. What is the Causal Effect of Knowledge on Preferences?

We use a novel field experiment which jointly tests two implicit assumptions of updating models in a joint framework: that new information leads to new knowledge and that new knowledge can affect economic decisions. In the experiment, we elicit subjects’ prior knowledge state about a good’s attributes, exogenously vary how much new information about good attributes we provide to subjects, elicit subjects’ valuation for the good, and elicit posterior knowledge states about the same good attributes. Testing for changes in knowledge jointly with changes in preferences allows us to horserace updating models more completely than previous studies since we observe ex ante and ex post knowledge states. Our results are consistent with a model of incomplete learning, fatigue and either confirmation bias or costly search coupled with unbiased priors.


LaRiviere Jacob, Czajkowski Mikołaj, Hanley Nick, Simpson Katherine
Learning Information Bayesian Updating Behavioral Economics

WP(10/2014)127. We all do it, but are we willing to admit? Incentivizing digital pirates' confessions.

Autorzy: Kukla-Gryz Anna, Krawczyk Michał, Siwiński Konrad, Tyrowicz Joanna
In this study we try to assess the prevalence of illicit downloading in the market of audio books and the willingness to admit to such practices. We compare the Bayesian Truth Serum (Prelec, 2004) treatment in which truthful responses and precise est…

WP127. We all do it, but are we willing to admit? Incentivizing digital pirates' confessions.

In this study we try to assess the prevalence of illicit downloading in the market of audio books and the willingness to admit to such practices. We compare the Bayesian Truth Serum (Prelec, 2004) treatment in which truthful responses and precise estimates are rewarded to the control treatment with a flat participation fee. We find a sizable treatment effect - incentivized ?pirates? admit approximately 60% more often than the non-incentivized ones.


Kukla-Gryz Anna, Krawczyk Michał, Siwiński Konrad, Tyrowicz Joanna
illegal download digital piracy Bayesian Truth Serum wages

WP(15/2012)81. The Effects of Experience on Preference Uncertainty: Theory and Empirics for Public and Quasi-Public Goods

Autorzy: Czajkowski Mikołaj, Hanley Nick, LaRiviere Jacob
This paper develops a model of demand estimation in which consumers learn about their true preferences through consumption experiences. We develop a theoretical model of Bayesian updating, perform comparative statics over the model, and show how the …

WP81. The Effects of Experience on Preference Uncertainty: Theory and Empirics for Public and Quasi-Public Goods

This paper develops a model of demand estimation in which consumers learn about their true preferences through consumption experiences. We develop a theoretical model of Bayesian updating, perform comparative statics over the model, and show how the theoretical model can be consistently incorporated into a reduced form econometric model. We then estimate the model using data collected for two quasi-public goods. We find that the predictions of the theoretical exercise that additional experience with a good will make consumers more certain over their preferences in both mean and variance are supported in each case.


Czajkowski Mikołaj, Hanley Nick, LaRiviere Jacob
Bayesian demand estimation stated preference generalized multinomial logit scale scale variance
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