Robustness of Price Perception: How Strong are Anchoring, Left-Digit- and Framing-Effects when Promoting Sales Offers?

While the large number of publications on behavioral pricing and partly spectacular results suggest that it is easily possible to influence parameters in the consumers´ price perception (for example, by providing price anchors as competitive prices, 99-price endings, information on a relative or absolute saving or information on availability of the prices by the suppliers), this empirical study based on experimental designs comes to a contrary conclusion: it turns out that the assessment of perceived value for money and cheapness for different sales promotion campaigns in Germany (train ticket, smartphone flat rate, filter coffee) is amazingly robust and that psychological factors tested in different experiments have a relatively low impact. The theoretical, practical and research implications of these findings are discussed

Determination of structure tilting in magnetized plasmas - Time delay estimation in two dimensions

Time delay estimation (TDE) is a well-known technique to investigate poloidal flows in fusion plasmas. The present work is an extension of the earlier works of A. Bencze and S. Zoletnik 2005 and B. T\'al et al. 2011. From the prospective of the comparison of theory and experiment it seem to be important to estimate the statistical properties of the TDE based on solid mathematical groundings. This paper provides analytic derivation of the variance of the TDE using a two-dimensional model for coherent turbulent structures in the plasma edge and also gives an explicit method for determination of the tilt angle of structures. As a demonstration this method is then applied to the results of a quasi-2D Beam Emission Spectroscopy (BES) measurement performed at the TEXTOR tokamak.Comment: 8 pages, 10 figure

Equivariant flow matching

Normalizing flows are a class of deep generative models that are especially interesting for modeling probability distributions in physics, where the exact likelihood of flows allows reweighting to known target energy functions and computing unbiased observables. For instance, Boltzmann generators tackle the long-standing sampling problem in statistical physics by training flows to produce equilibrium samples of many-body systems such as small molecules and proteins. To build effective models for such systems, it is crucial to incorporate the symmetries of the target energy into the model, which can be achieved by equivariant continuous normalizing flows (CNFs). However, CNFs can be computationally expensive to train and generate samples from, which has hampered their scalability and practical application. In this paper, we introduce equivariant flow matching, a new training objective for equivariant CNFs that is based on the recently proposed optimal transport flow matching. Equivariant flow matching exploits the physical symmetries of the target energy for efficient, simulation-free training of equivariant CNFs. We demonstrate the effectiveness of our approach on many-particle systems and a small molecule, alanine dipeptide, where for the first time we obtain a Boltzmann generator with significant sampling efficiency without relying on tailored internal coordinate featurization. Our results show that the equivariant flow matching objective yields flows with shorter integration paths, improved sampling efficiency, and higher scalability compared to existing methods

U(1)-Symmetry breaking and violation of axial symmetry in TlCuCl3 and other insulating spin systems

We describe the Bose-Einstein condensate of magnetic bosonic quasiparticles in insulating spin systems using a phenomenological standard functional method for T = 0. We show that results that are already known from advanced computational techniques immediately follow. The inclusion of a perturbative anisotropy term that violates the axial symmetry allows us to remarkably well explain a number of experimental features of the dimerized spin-1/2 system TlCuCl3. Based on an energetic argument we predict a general intrinsic instability of an axially symmetric magnetic condensate towards a violation of this symmetry, which leads to the spontaneous formation of an anisotropy gap in the energy spectrum above the critical field. We, therefore, expect that a true Goldstone mode in insulating spin systems, i.e., a strictly linear energy-dispersion relation down to arbitrarily small excitations energies, cannot be observed in any real material.Comment: 6 pages, 3 figure

Temperature steerable flows and Boltzmann generators

Boltzmann generators approach the sampling problem in many-body physics by combining a normalizing flow and a statistical reweighting method to generate samples in thermodynamic equilibrium. The equilibrium distribution is usually defined by an energy function and a thermodynamic state. Here, we propose temperature steerable flows (TSFs) which are able to generate a family of probability densities parametrized by a choosable temperature parameter. TSFs can be embedded in generalized ensemble sampling frameworks to sample a physical system across multiple thermodynamic states

Skipping the Replica Exchange Ladder with Normalizing Flows

We combine replica exchange (parallel tempering) with normalizing flows, a class of deep generative models. These two sampling strategies complement each other, resulting in an efficient strategy for sampling molecular systems characterized by rare events, which we call learned replica exchange (LREX). In LREX, a normalizing flow is trained to map the configurations of the fastest-mixing replica into configurations belonging to the target distribution, allowing direct exchanges between the two without the need to simulate intermediate replicas. This can significantly reduce the computational cost compared to standard replica exchange. The proposed method also offers several advantages with respect to Boltzmann generators that directly use normalizing flows to sample the target distribution. We apply LREX to some prototypical molecular dynamics systems, highlighting the improvements over previous methods

Demystifying the "Sunk Cost Fallacy": When Considering Fixed Cost in Decision-Making is Reasonable

Economic theory explains that when making decisions, historical costs should be irrelevant. When people are influenced by sunk costs in their decision-making, they are said to be committing the “sunk cost fallacy”, summarized by Kelly (2004) as the conjunction of two claims: (1) Individuals often do give weight to sunk costs in their decision-making, and (2) it is irrational for them to do so. Based on three studies both aspects are investigated (Amazons loyalty program Prime, German railways discount card BahnCard and decisions to use the own car when making long-haul trips). There are strong indicators that in all three examples fixed costs play a crucial role when consumers make decisions; and doing so is not necessarily irrational