Mouse-in-Hole Problem and Recursion Concepts
48 Questions
4 Views

Choose a study mode

Play Quiz
Study Flashcards
Spaced Repetition
Chat to Lesson

Podcast

Play an AI-generated podcast conversation about this lesson

Questions and Answers

In the mouse-in-hole problem, what is the expected time for the mouse to reach its goal, given the recursive nature of its choices?

  • 20 minutes
  • 30 minutes (correct)
  • 10 minutes
  • 45 minutes
  • What does the policy function, $c_t^* = \phi(k_t)$, represent in the context of recursive problems?

  • The maximum achievable utility at time t.
  • The initial condition of the state variable.
  • The constraint on the state variable's evolution.
  • The optimal control variable choice given the current state. (correct)
  • In recursive problems, how is the value function, $V_0(k_0)$, defined?

  • The utility derived from the control variable at time 0.
  • The discounted sum of state variables.
  • The maximum utility achievable from the initial state. (correct)
  • The minimum utility achievable from the initial state.
  • What is the role of the discount factor, $\beta$, in recursive problems?

    <p>To decrease the value of future utility. (D)</p> Signup and view all the answers

    In the equation $k_{t+1} = g(k_t, c_t)$, what do $k_t$ and $c_t$ represent respectively?

    <p>State variable and control variable. (C)</p> Signup and view all the answers

    How does the recursive approach help in solving the mouse-in-hole problem?

    <p>It breaks down the problem into smaller, self-similar subproblems. (D)</p> Signup and view all the answers

    In the context of the Bellman equation and the 'guess-&-verify' method, what is a key limitation?

    <p>Closed-form solutions for the value function $V(k)$ are only possible for a limited set of payoff functions $u(c_t)$ and dynamic constraints $k_{t+1} = g(k_t, c_t)$. (C)</p> Signup and view all the answers

    Why is the Envelope Theorem useful when analyzing a Bellman problem, even if an analytical formulation of $V(k)$ is unavailable?

    <p>It can help identify characteristics of the solution without needing to find the exact analytical form of $V(k)$. (D)</p> Signup and view all the answers

    What is the critical assumption needed before using the Envelope Theorem on a value function?

    <p>The value function must be differentiable. (C)</p> Signup and view all the answers

    Given the equation $V'(k_0) = \beta V'(k_1) g_k(k_0, c_0^)$ derived from the Envelope Theorem, what does $g_k(k_0, c_0^)$ represent?

    <p>The partial derivative of the function $g$ (dynamic constraint) with respect to $k_0$, evaluated at the optimal consumption $c_0^*$. (D)</p> Signup and view all the answers

    With log utility ($\text{u(c) = log(c)}$) in an economic model, what can be generally inferred about the savings rate?

    <p>Constant. (D)</p> Signup and view all the answers

    What does the expression $k = (\alpha\beta)^{\frac{1}{1-\alpha}}$ typically represent in the context of economic models?

    <p>The steady-state level of capital. (D)</p> Signup and view all the answers

    If $V'(k_0) = b/k_0 > 0$, what could be said about welfare?

    <p>Welfare increases with $k_0$. (D)</p> Signup and view all the answers

    In the expression $\alpha - c_0^* = (1 - \alpha\beta)k_0^{\alpha}$, how does the parameter $\alpha$ most directly impact the economic model?

    <p>It influences the productivity of capital. (A)</p> Signup and view all the answers

    In the context of recursive problems, what does the notation $V_1(k_1)$ represent?

    <p>The optimal value function starting from period 1 with initial state $k_1$. (B)</p> Signup and view all the answers

    What is the 'Principle of Optimality' in the context of dynamic programming?

    <p>An optimal policy has the property that, whatever the initial state and initial decision, the remaining decisions must constitute an optimal policy with respect to the state resulting from the first decision. (D)</p> Signup and view all the answers

    In the Bellman equation $V_0(k_0) = max_{c_0} {u_0(c_0) + \beta V_1(k_1)}$, what does $\beta$ represent?

    <p>The discount factor, reflecting the decision-maker's preference for present versus future utility. (B)</p> Signup and view all the answers

    What is the role of the function $g(k_t, c_t)$ in the recursive formulation?

    <p>It represents the production function, determining how capital and consumption in period t determine the capital stock in period t+1. (D)</p> Signup and view all the answers

    What does the Bellman equation allow us to do when solving dynamic optimization problems?

    <p>Convert a multi-period optimization problem into a recursive form, simplifying the solution process. (D)</p> Signup and view all the answers

    Consider optimizing consumption over two periods. How does an increase in $c_0$ affect $V_1(k_1)$?

    <p>An increase in $c_0$ decreases $k_1$, and thereby influences $V_1(k_1)$. (C)</p> Signup and view all the answers

    In the context of an infinite-horizon problem (T → ∞), what is a key simplification that arises when using the Bellman equation?

    <p>The problems in period 0 and period 1 become identical except for their initial conditions. (B)</p> Signup and view all the answers

    What is the relationship between $U_0$ and $V_0(k_0)$?

    <p>$U_0$ is the instantaneous utility derived from consumption at time 0, while $V_0(k_0)$ represents the total value from time 0 onwards. (B)</p> Signup and view all the answers

    In the context of the functional operator T, what does it mean for V(k) to be a 'fixed point'?

    <p>Applying the operator T to V(k) leaves V(k) unchanged. (D)</p> Signup and view all the answers

    What is the significance of finding a fixed point for the functional operator T in the context of value function iteration?

    <p>It confirms that the solution, V(k), is optimal and stable. (C)</p> Signup and view all the answers

    What is the result of maximizing the right-hand side (RHS) of the equation in the text?

    <p>$y = \alpha \beta k_t^\alpha$ (B)</p> Signup and view all the answers

    In the stochastic Ramsey model, what condition must be met for the random variable $ϵ_t$?

    <p>It must be independently and identically distributed with a zero mean conditional on the past. (B)</p> Signup and view all the answers

    Consider the expression $\lim_{n \to \infty} (T^n V)(k_t)$. What does this limit represent in the context of value function iteration?

    <p>The value function $V(k_t)$. (B)</p> Signup and view all the answers

    In the context of the guessed value function $V(k_t, A_t) = a + b_k \ln k_t + b_A \ln A_t$, what does $b_k$ represent within the stochastic Ramsey model?

    <p>The sensitivity of the value function to changes in capital stock. (B)</p> Signup and view all the answers

    What is the role of the functional operator, T, in the value function iteration process?

    <p>It updates the value function estimate in each iteration. (B)</p> Signup and view all the answers

    How does the equation $(T V̂)(kt) = T(T V̂)$ relate to the concept of a fixed point?

    <p>It mathematically expresses that applying the operator T does not change the value function at the fixed point. (A)</p> Signup and view all the answers

    What is the implication of the statement $k_{t+1} ∈ [0, f(k_t)]$ in the finite horizon problem?

    <p>Capital stock in the next period must be non-negative and cannot exceed the total output of the current period. (D)</p> Signup and view all the answers

    In the stochastic Ramsey model, how is the optimal consumption $c_0^*$ determined?

    <p>As a constant fraction of current capital stock and technology. (D)</p> Signup and view all the answers

    Suppose $\alpha = 0.3$ and $\beta = 0.9$. What is the value of the expression $\frac{\alpha \beta}{1 - \alpha \beta}$?

    <p>0.387 (A)</p> Signup and view all the answers

    What does the equation $u'(c_0^) + β E_0[V_k(k_1, A_1)g_c(k_1, A_1, c_0^)] = 0$ represent in the context of the provided content?

    <p>The Euler equation, representing an interior solution for optimal consumption. (C)</p> Signup and view all the answers

    In the limit, what happens to the term $(1 + \beta + \beta^2 + ... + \beta^{n-1})$ as $n$ approaches infinity, assuming $0 < \beta < 1$?

    <p>It converges to $\frac{1}{1 - \beta}$. (D)</p> Signup and view all the answers

    Why is the value function in the finite horizon problem indexed by time, $V_t(k_t)$?

    <p>To reflect that the functional form of the value function may change over time as the horizon approaches. (A)</p> Signup and view all the answers

    Given $V(k_0, A_0) = \max_{c_0} {\ln(c_0) + β E_0[V(k_1, A_1)]}$ and $k_1 = g(k_0, c_0) = A_0k_0^α - c_0$, what is being maximized?

    <p>The present value of lifetime utility. (B)</p> Signup and view all the answers

    In the guessed and verified solution for $V(k_t, A_t)$, what role does the parameter $ρ$ play?

    <p>It governs the persistence of technology shocks. (A)</p> Signup and view all the answers

    In the given context, how does the equation $V'(k_0) = \beta V'(k_1)g_k(k_0, c_0^*)$ relate to the optimal consumption path?

    <p>It equates the marginal utility of consumption at time 0 to the discounted marginal utility of consumption at time 1, adjusted by the capital's marginal product. (D)</p> Signup and view all the answers

    Given $u'(c_0^) + \beta V'(k_1)g_c(k_0, c_0^) = 0$, what is the economic interpretation of the term $\beta V'(k_1)g_c(k_0, c_0^*)$?

    <p>The present value of the marginal utility of consuming one less unit today, invested to increase future capital. (A)</p> Signup and view all the answers

    How does the Euler equation, $u'(c_0^) = \beta \frac{g_k(k_1, c_1^)g_c(k_0, c_0^)}{g_c(k_1, c_1^)} u'(c_1^*)$, simplify in the context of $k_{t+1} = (1 + r)k_t + w - c_t$?

    <p>$u'(c_0^<em>) = \beta (1 + r) u'(c_1^</em>)$ (B)</p> Signup and view all the answers

    Why is the Euler equation often preferred for numerical solutions compared to the Bellman equation?

    <p>The Euler equation is a first-order condition that avoids dealing directly with the value function. (D)</p> Signup and view all the answers

    What does $g_k(k_0, c_0^*)$ represent in the context of optimal control?

    <p>The marginal product of capital, indicating how much output increases with an additional unit of capital. (B)</p> Signup and view all the answers

    What is the role of $\beta$ (beta) in the presented equations?

    <p>It represents the discount factor, reflecting the preference for present versus future consumption. (C)</p> Signup and view all the answers

    How does an increase in $r$ (the interest rate) affect the Euler equation $u'(c_0^) = \beta (1 + r) u'(c_1^)$?

    <p>It decreases current consumption ($c_0^<em>$) and increases future consumption ($c_1^</em>$). (C)</p> Signup and view all the answers

    Given the equation $k_{t+1} = g(k_t, c_t) = (1 + r)k_t + w - c_t$, what does 'w' represent?

    <p>The wage rate or labor income. (A)</p> Signup and view all the answers

    In the equation $u'(c_0^) = \beta \frac{g_k(k_1, c_1^)g_c(k_0, c_0^)}{g_c(k_1, c_1^)} u'(c_1^)$, what happens if $g_c(k_0, c_0^) > g_c(k_1, c_1^*)$?

    <p>Current consumption becomes relatively more valuable than future consumption. (C)</p> Signup and view all the answers

    What would be the likely effect of a government policy that effectively increases the discount factor $\beta$ for all consumers?

    <p>An increase in savings and investment. (C)</p> Signup and view all the answers

    Flashcards

    Mouse's Hole Choice Probability

    The mouse chooses to enter each hole with a probability of 1/3.

    Expected Time to Goal (E[T])

    The average time the mouse takes to reach its goal based on the probabilities of choosing holes.

    Conditional Expected Time [T | i]

    Expected time to goal given the mouse entered hole i.

    Recursive Problem

    A problem that can be defined in terms of itself, using previous results to solve for future ones.

    Signup and view all the flashcards

    Utility Function U(c_t)

    A function that reflects the satisfaction or value gained from consumption at time t.

    Signup and view all the flashcards

    Control Variable (c_t)

    A variable that represents decisions made at each time step in a dynamic model.

    Signup and view all the flashcards

    State Variable (k_t)

    A variable that describes the state of a system at time t, impacting future decisions.

    Signup and view all the flashcards

    Value Function V(k_0)

    The maximum overall utility obtainable at time t given the initial state variable.

    Signup and view all the flashcards

    Decision-maker's objective function

    The formulation for maximizing utility over a time span, incorporating current and future utility.

    Signup and view all the flashcards

    Optimal choice at t=0

    The initial decision that maximizes utility based on current state and future expectations.

    Signup and view all the flashcards

    State transition function

    Describes how the state changes based on the decision and the previous state.

    Signup and view all the flashcards

    Principle of optimality

    An optimal policy will remain optimal even after the first decision is made.

    Signup and view all the flashcards

    Bellman equation (BE)

    A recursive formula representing the relationship between the value of the current state and future states.

    Signup and view all the flashcards

    Value function V1(k1)

    Function that gives the maximum utility achievable from a given state in the next period.

    Signup and view all the flashcards

    Constant Saving Rate

    In log utility, the saving rate remains steady over time.

    Signup and view all the flashcards

    Steady State Capital

    In steady state, capital k equals (αβ) raised to the power of (1 - α).

    Signup and view all the flashcards

    Welfare and k0 Relationship

    Welfare increases as capital k0 increases, indicating positive effects.

    Signup and view all the flashcards

    Envelope Theorem

    A method for analyzing value functions without needing closed-form solutions.

    Signup and view all the flashcards

    Differentiable Value Function

    The Envelope Theorem assumes the value function is differentiable.

    Signup and view all the flashcards

    V(k0) Equation

    V(k0) combines utility from current consumption and the future value function.

    Signup and view all the flashcards

    Simplified V′(k0)

    Via the Envelope Theorem, V′(k0) simplifies involving β and gk.

    Signup and view all the flashcards

    Dynamic Constraint

    The relationship kt+1 = g(kt, ct) shows how current decisions affect future capital.

    Signup and view all the flashcards

    Euler Equation

    An equation connecting current and future utility based on consumption.

    Signup and view all the flashcards

    First-Order Condition

    A necessary condition for optimality in a dynamic program relating to utility derivatives.

    Signup and view all the flashcards

    Marginal Utility u′(c)

    The additional satisfaction gained from consuming an extra unit of good c.

    Signup and view all the flashcards

    Production Function g(k, c)

    Describes how current capital and consumption affect future state variables.

    Signup and view all the flashcards

    Intertemporal Choice

    Decision making where outcomes depend on consumption over different time periods.

    Signup and view all the flashcards

    Dynamic Programming

    A method for solving complex problems by breaking them into simpler subproblems.

    Signup and view all the flashcards

    Transition Equation

    An equation describing how the state variable evolves over time.

    Signup and view all the flashcards

    Numerical Solution Method

    A technique employed to find approximate solutions to equations, especially in dynamic contexts.

    Signup and view all the flashcards

    Functional operator T

    A mathematical operator applied to a function to generate a new function.

    Signup and view all the flashcards

    Fixed point

    A point where T V(k_t) equals V(k_t), indicating stability.

    Signup and view all the flashcards

    Maximization in equations

    Finding the highest value or maximum of an expression under defined constraints.

    Signup and view all the flashcards

    Iteration process

    Repeating steps to approach a desired result, often used in functions.

    Signup and view all the flashcards

    Convergence of sequences

    The process where a sequence approaches a specific value as steps increase.

    Signup and view all the flashcards

    Logarithm properties

    Rules governing logarithmic functions, such as ln(a*b) = ln(a) + ln(b).

    Signup and view all the flashcards

    Dynamic model

    A system that changes in response to decisions over time.

    Signup and view all the flashcards

    c*

    The optimal consumption level that maximizes utility.

    Signup and view all the flashcards

    k1

    Future capital level after consumption decisions are made.

    Signup and view all the flashcards

    V(k, A)

    The value function that represents the maximum utility obtainable.

    Signup and view all the flashcards

    u(c)

    Utility function representing satisfaction from consumption.

    Signup and view all the flashcards

    Markov process

    A stochastic process with the Markov property, dependent only on the current state.

    Signup and view all the flashcards

    Backward induction

    A method of solving dynamic programming problems starting from final conditions.

    Signup and view all the flashcards

    g(k, A, c)

    Function that determines future capital based on current capital, productivity, and consumption.

    Signup and view all the flashcards

    β (beta)

    A discount factor indicating the value of future utility.

    Signup and view all the flashcards

    Study Notes

    Applied Macroeconomics: Dynamic Programming in Discrete Time

    • The presentation covers dynamic programming applications in discrete time.
    • Dynamic programming elegantly frames multi-period problems as a recursive structure, with current decisions and future conditions influencing the optimal path.
    • Traditional Lagrangian/Hamiltonian approaches can become complex with random state variables (uncertainty). Dynamic programming offers a simpler method.
    • The presentation uses introductory examples, like a mouse searching for food, to highlight recursive problem-solving.
    • The expected time to reach the goal for the mouse is derived using recursive computations.
    • The recursive structure of problems involves considering one period ("today") and the future ("rest of the horizon").
    • Under specific conditions, the future component's functional form matches the overall problem form, simplifying calculation.
    • Key elements of dynamic programming include maximum utility across time, constraints like resource or capital evolution, and a discount rate.
    • The value function is a cornerstone. It estimates the best attainable utility from a given state in a problem given an optimal behavioral strategy.
    • Various tools and solution methods exist. These include "guess-and-verify," envelope theorems, and backward induction.
    • The presented material addresses uncertainty and finite-horizon situations.
    • Stochastic processes are used to model randomness over time.
    • Dynamic programming models can handle uncertainty through expected utility calculations.
    • The value function concept is crucial, encompassing the optimal choices expected from a starting condition.
    • For the specific issue of infinite horizons, there's an assumption that future expectations don't differ from today's expectations.
    • Different approaches to deriving parameters to solve using value-iteration exist.
    • For finite horizons, the value function's form changes over time. This method of solution is through backward induction.
    • The finite horizon case shows how optimal choices depend on the current state and the remaining periods until the end.
    • Theorem I and II demonstrate conditions under which unique and continuous solutions in dynamic programming contexts can be found.

    Recursive Problems

    • The maximum utility is optimized over a set of choices. These choices often come in the form of consumption (c), which must meet constraints. These constraints include the evolution of state variables, like capital (k).
    • Bellman equation formulation is shown as a means to solve dynamic programming problems.
    • The concept of the value function is a key element in recursive methods of analysis. The value function sums up the optimal future behaviors given an initial condition
    • The value function is independent of time when looking at infinite futures meaning that the decision-maker's perspective at time zero and when they are looking into the future are identical.

    Value Function

    • The value function typically focuses on the utility obtained at each time period with an optimal strategy in place
    • The value function can be found using various numerical approaches.
    • For non-finite horizons, the value function is independent of time.
    • The value function characterization methods, like the envelope theorem, don't require a direct calculation of the value function. However, the function needs to satisfy the necessary conditions to achieve a solution.

    Envelope Theorem

    • The value function is often differentiable, enabling the use of the envelope theorem in determining parameters.
    • Envelope theorem methods can eliminate the need to solve the value function directly and can be applied for constrained optimization problems.

    Uncertainty

    • When introducing randomness, stochastic processes (having the Markov property) are commonly used. Randomness considerations are shown to simplify the problem by considering current decisions and their uncertainty
    • This method relies on expected utility calculations.

    Guess-and-Verify

    • This approach applies primarily when a closed-form solution for the value function exists. An example of a problem with an analytic solution (discrete-time Ramsey model) is demonstrated, in addition to its specific solution.
    • It works by making an educated guess about the value function and then testing to see if that structure meets the necessary conditions.

    Appendix (Finite Horizon)

    • Dynamic programming problems with finite horizons can be addressed using backward induction.
    • Backward induction begins with the last period.
    • Each period's optimal solution is derived based on the solutions of subsequent periods.

    Studying That Suits You

    Use AI to generate personalized quizzes and flashcards to suit your learning preferences.

    Quiz Team

    Related Documents

    Dynamic Programming 2024 PDF

    Description

    This quiz explores the intricacies of the mouse-in-hole problem, focusing on expected time calculations, policy functions, and value functions in recursive settings. Additionally, it delves into the significance of the discount factor and the limitations of methods like the Bellman equation in solving recursive problems.

    More Like This

    Mouse Operations Quiz
    18 questions
    Key Concepts in Mouse Genetics
    14 questions
    Mouse Genetics Flashcards
    20 questions
    Common Mouse Actions Quiz
    16 questions
    Use Quizgecko on...
    Browser
    Browser