Essays on Decision Making: Intertemporal Choice and Uncertainty

Being labeled as a social science, much of economics is about understanding human behavior; be it in the face of uncertainty or delayed payoffs through time or strategic situations such as auctions, bargaining, and so on. This thesis will be concerned with the first two, namely uncertainty and time preferences. The main focus of this thesis is what we can summarize with two broad titles: "irrationalities" in human behavior and an alternative perspective on 'rational behavior". My claim requires a clarification of what is meant by rational or irrational behavior. In one of the early discussions of this topic, Richter (1966) defined a rational consumer as someone for whom there exists a total, reflexive, and transitive binary relation on the set of commodities so that his choice data consists of maximal elements of this binary relation. In this respect, Richter (1966) only imposed minimal consistency conditions on behavior for it to be labeled as rational. Although his setting does not involve any uncertainty or time dimension, analogues of these conditions exist for the models we consider here as well. So one can extend the rationality notion of Richter (1966) to our models too. Yet the essence of his approach to rationality is different than the one we take up in this thesis. This minimalistic approach of Richter would leave little space for discussions on rational behavior because much behavior would be rational except for a few cleverly constructed counterexamples. Instead we will consider more widely accepted norms of rationality and analyze them in the framework of uncertainty and time preferences. The widely accepted norms of rationality mentioned above are understood to be axioms that lead to decision rules describing people's behavior. In the case of decision making under risk and uncertainty the most commonly used decision model is expected utility, and in the case of dynamic decision making, it is the constant discounted utility model. Although there are models that combine both to explain decision making in a dynamic stochastic settings, in this thesis we study them in isolation to assess the nature of the models in more detail

Effects of topically applied contractubex® on epidural fibrosis and axonal regeneration in injured rat sciatic nerve

AIM: To investigate the effects of Contractubex (R) (Cx) on peripheral nerve regeneration and scar formation. MATERIAL and METHODS: A surgical procedure involving sciatic nerve incision in 24 adult male Sprague-Dawley rats followed by epineural suturing was performed. In weeks 4 and 12 following surgery, macroscopic, histological, functional, and electromyographic examinations of the sciatic nerve were conducted. RESULTS: No significant difference was found between the Cx group and the control group in terms of sciatic function index (SFI) and distal latency results at week 4 (p>0.05). However, significant improvements in the Cx group were observed in SFI amplitudes and nerve action potentials at week 12 (p<0.001 and p<0.001, respectively). Significant improvements were found in the amplitudes of nerve action potentials in the treatment group after weeks 4 and 12 (p<0.05 and p<0.001, respectively). Macroscopically and histopathologically, epidural fibrosis decreased (p<0.05 and p<0.001, respectively). For both measurement times, the treatment group had significantly higher numbers of axons (week 4, p<0.05; week 12, p<0.001), and the treatment group had better results regarding its axon area (weeks 4 and 12, p<0.001) and myelin thickness (weeks 4 and 12, p<0.05). CONCLUSION: Cx, which is applied topically in peripheral nerve injury, affects axonal regeneration and axonal maturation positively and reduces the functional loss

Design and Beam Dynamics Studies for 5 GeV Electron Linac in CLIC Positron Source

CLIC (Compact Linear Collider) and ILC (International Linear Collider) are the two future linear collider projects after LHC (Large Hadron Collider) era. Both are planning to accelerate electrons and positrons to collide at a centre-of-mass energy greater than 250 GeV. CLIC is foreseen to be constructed in several stages corresponding to different centre-of-mass energies. The first at 380 GeV would be followed by stages at 1.5 and 3 TeV. What makes CLIC unique is the two-beam acceleration scheme. Drive beam reaching a current of about 100 A at a beam energy of about 2.4 GeV is transported through the main linac tunnel and power both main electron and positron beams. One of the most important part is injection of main beams to the main linac tunnel. Electrons are produced by an electron gun and transport through different energy linacs to the main linac. For the positron production, 5 GeV electron beam is planned to be used by impinging to amorphous tungsten target. This thesis is based on the design and optimization of parameters as well as effect of instabilities on designed beamline for this 5 GeV electron linac for positron production chain

Vehicle sales prediction using neural fuzzy logic method in industry 4.0

In the 20th century, 4th Industrial revolution came true when the technological developments and applying these technologies were used in Industry. The machines began to share the information about their product and services in real time in production with the system, which is called Industry 4.0. The artificial intelligence develops which is the one of the most major developments of 20th century. In this study, the first three months sales of 2017 are tried to be predicted with neural fuzzy logic method which is the sub discipline of the artificial intelligence by using as data which are taken from the monthly sales of domestic and imported automobiles, the monthly sales of domestic and imported light commercial vehicles, monthly producer prices, domestic producer prices index on a monthly basis in Turkey between 2014 and 2016, S&P and Moody’s’ quarterly reports on Turkey. Applied model has reached the 93 % accuracy

The Effect of Learning on Ambiguity Attitudes

International audienceThis paper studies the effect of learning information on people’s attitudes toward ambiguity. We propose a method to separate ambiguity attitudes from subjective probabilities and to decompose ambiguity attitudes into two components. Under models like prospect theory that represent ambiguity through nonadditive decision weights, these components reflect pessimism and likelihood insensitivity. Under multiple priors models, they reflect ambiguity aversion and perceived ambiguity. We apply our method in an experiment where we elicit the ask prices of options with payoffs depending on the returns of initial public offerings (IPOs) on the New York Stock Exchange. IPOs are a natural context to study the effect of learning, as prior information about their returns is unavailable. Subjects perceived substantial ambiguity and they were insensitive to likelihood information. We observed only little pessimism and ambiguity aversion. Subjective probabilities were well calibrated and close to the true frequencies. Subjects’ behavior moved toward expected utility with more information, but substantial deviations remained even in the maximum information condition