Memory limitations lie at the core of how agents store past information and make decisions. For example, consider a buyer choosing between two alternative brands of a good. In thinking about a brand, she may recall her past experiences, product reviews, or expert’s ratings, which induce a belief about the brand’s quality. In this process, the buyer may divide the quality scale into finitely many categories such as high, medium, and low. Then, she may switch from one brand to another only if her belief about a brand’s quality moves from one category to another. Although memory limitations in the form of finite memory naturally arise in many facets of economic life, formal economic models that incorporate the finite memory restriction are rather limited. The aim of this project is to understand the implications of finite memory for dynamic decision problems in both choice theoretical and strategic economic contexts. These implications are significant to reach out a wide range of objectives such as obtaining better predictions of consumer’s choices, unravelling the observed equilibrium behaviour, and regulating markets as to maximize social welfare. In addition, the results to be obtained can provide new explanations for observed economic phenomena such as limited attention and price stickiness. The outgoing phase of this GF project is divided into two parts. This first part of the project aims at addressing a fundamental problem to expedite the use of finite memory model in economic applications. In the second part, I aim to examine how strategic pricing aspects of a dynamic duopoly (oligopoly) model. The goal in here is to incorporate memory limitations in the form of finite memory into a fundamental economic problem, and identify its effects.This GF project will be carried out in Princeton University under the supervision of Prof. Faruk Gul. I will then return to Bilkent University to improve my results for 12 months under the supervision of Prof. Semih Koray.

3D (3-dimensional) Image Synthesis (3DIS) is a technology to render objects from different views which enables numerous applications in computer graphics and computer vision. As the digital world is becoming more crucial especially in the times of pandemic, 3DIS can provide tools for online classes, virtual social tours, improved gaming experience and simulators for robotics by providing realistic virtual 3D environments. Furthermore, 3DIS by disentangling the attributes of objects and entangling them via a renderer for synthesize, can provide a technology to learn useful features from our visual world that can be used for video understanding, one of the biggest goals of artificial intelligence. Here, I propose 3DIS-NN, a set of methods to improve the quality of 3DIS with deep neural networks (DNNs), and bring it close to the production quality, which will contribute to the European Union’s Future and Emerging Technology ambitions of Horizon Europe. Learning 3DIS from 2D images with deep learning is a challenging topic due to its inherent ambiguity. 3DIS-NN will enable high-quality 3DIS results by i) creating a dataset with weak labels to feed the data-hungry DNNs for better accuracy, ii) improving robustness of 3D geometry and texture prediction from images, iii) handling the impurities in segmentation of objects with a novel design of architecture, and iv) providing a tool to further close the domain gap in renderers and real images. This interdisciplinary proposal which is at the intersection of deep learning and computer graphics will be carried out at under the supervision of Prof. Ugur Gudukbay who is an expert in computer graphics. In terms of career developments, this proposal will consolidate and accelerate my career on the international landscape scene as a pioneer lead authority in the new cross-disciplinary area of “deep learning & computer graphics”.