Coursehero >> Iowa >> Iowa State >> HCI 504

Course Hero has millions of student submitted documents similar to the one below including study guides, homework solutions, papers, exam answer keys and textbook solutions.

Institutions Labor and Market Performance An Agent-Based Computational Economics Approach Presenter: Leigh Tesfatsion Professor of Economics and Mathematics Department of Economics Iowa State University Ames, Iowa 50011-1070 http://www.econ.iastate.edu/tesfatsi/ tesfatsi@iastate.edu 1 Outline What is Agent-Based Computational Economics (ACE)? Labor Institutions and Market Performance: What does ACE have to offer? Illustration: (M. Pingle/L. Tesfatsion, 2003) Evolution of Worker-Employer Networks and Behaviors Under Alternative Non-Employment Benefits 2 What is Agent-Based Computational Economics (ACE)? Culture-dish approach to the study of decentralized market processes Computational study of economic processes modeled as dynamic systems of interacting agents ACE Resource Site: www.econ.iastate.edu/tesfatsi/ace.htm 3 ACE Modeling: Culture Dish Analogy Modeler constructs a computational economic world populated by various types of agents (economic, social, biological, & physical) Modeler sets initial conditions The world then develops over time without further outside intervention World driven solely by agent interactions 4 ACE Modeling: Culture Dish Analogy Experimental Treatment Factors (Initial Conditions) Economy Develops Over Time (Culture Dish) Macro Regularities 5 Key Characteristics of ACE Models Agents are encapsulated software programs capable of Adaptation to environmental conditions Social communication with other agents Goal-directed learning Autonomy (self-activation and self-determinism based on private internal processes) Agents can be situated in realistically rendered problem environments Behaviour/interaction patterns can develop endogenously over time 6 Current ACE Research Areas (http://www.econ.iastate.edu/tesfatsi/aapplic.htm ) Embodied cognition Network formation Evolution of norms Labor Markets Industrial organization Multiple-market modeling Technological change and economic growth Market design Automated markets and software agents Parallel experiments (real and computational agents) Many others... 7 Labor Institutions and Market Performance Some Key Issues: Labor contracts typically incomplete Supplemented by government programs with numerous eligibility restrictions Difficult to test program effects by means of conventional analytical and/or statistical tools 8 Unemployment Benefits (UB) UB only paid to no fault of their own unemployed UB recipients must continue to seek employment UB levels based on past earnings UB of limited duration UB financed by taxes imposed on employers Additional UB often granted when unemployment rate is abnormally high for prolonged periods 9 Example: U.S. Programs Providing Empirical Findings (Handbook of Labor Economics, Elsevier, 1999) Higher benefit level increases duration of unemployment spells. Increased benefit duration increases unemployment rate (unemployed as percentage of labor force). Evidence of other impacts of UB is considerably more mixed (endogeneity, small sample bias problems,...) 10 Common Approach to UB Modeling Dynamic Programming (DP) Jobs arise and end randomly Unemployed receive UB Workers compare DP value of new job vs. current job or unemployment Each worker maximizes lifetime expected utility Precise predictions, but empirical support unclear. 11 Potential Contributions of an ACE Approach to Labor Research www.econ.iastate.edu/tesfatsi/alabor.htm Employers/workers can be modeled as autonomous interacting agents Matching process can be preferential (endogenous hires, quits, and firings) Learning can be calibrated to data (empirical, human-subject experimental) Evolution of behaviors/interaction networks Relatively easy to incorporate realistically detailed structural features (market protocols, policy rules, program eligibility requirements, ) 12 An ACE Study of Non-Employment Payments (NEP) Evolution of Worker-Employer Networks and Behaviors under Alternative NonEmployment Benefits: An ACE Study Joint work with M. Pingle (U of Nevada-Reno) Published in New Directions in Networks, 2003, Edward-Elgar volume, edited by A. Nagurney Pre-print available at http://www.econ.iastate.edu/tesfatsi/alabmplt.pdf Parallel human-subject experiment conducted 13 ACE Labor Market Framework W1 W2 W3 ... W12 E1 E2 E3 ... E12 Preferential job search with choice/refusal of partners: Red directed arrow indicates refused work offer. 14 ACE Labor Market Framework... 12 workers with same observable attributes in initial period T=0 12 employers with same observable attributes in initial period T=0 Each worker can work for at most one employer in each period T Each employer can provide at most one job opening in each period T Worksite strategies in initial period T=0 are random and private info 15 Each worker and employer has Publicly available information about various market/policy protocols (e.g., NEP eligibility rules) Private behavioral methods that can evolve over time Privately stored data that can change over time 16 A Computational Worker Public Access: // Public Methods Protocols governing job search Protocols governing negotiations with potential employers Protocols governing non-employment payments program Methods for retrieving stored Worker data Private Access Only: // Private Methods Method for calculating my expected utility assessments Method for calculating my actual utility outcomes Method for updating my worksite strategy (learning) learning // Private Data Data about myself (my history, utility fct., current wealth ) Data recorded about external world (employer behaviors, ) Addresses for potential employers (permits communication) 17 A Computational Employer Public Access: // Public Methods Protocols governing search for workers Protocols governing negotiations with potential workers Protocols governing non-employment payments program Methods for retrieving stored Employer data Private Access Only: // Private Methods Method for calculating my expected profit assessments Method for calculating my actual outcomes profit Method for updating my worksite strategy (learning) learning // Private Data Data about myself (my history, profit fct., current wealth ) Data recorded about external world (worker behaviors, ) Addresses for potential workers (permits communication) 18 Flow of Activities in the ACE Labor Market Workers make offers to preferred employers at a small cost per offer (quits allowed) Employers accept or refuse received work offers (firings allowed) Each matched pair engages in one worksite interaction (PD game - cooperate or defect) After 150 work periods, each worker and employer updates its worksite IPD strategy 19 Flow of Activities in the ACE Labor Market Initialization Do 1000 Loops Search/Match Worksite Interactions Update Expectations Work Period: Do 150 Loops Evolve Worksite Strategies 20 Evolution Step: Worksite Interactions as Prisoner s Dilemma (PD) Games Employer C D C (40,40) (10,60) Worker D (60,10) (20,20) D = Defect (Shirk); C = Cooperate (Fulfill Obligations) 21 Key Issues Addressed How do changes in the level of the non-employment payment (NEP) affect... Worker-Employer Interaction Networks Worksite Behaviors: Degree to which workers/employers shirk (defect) or fulfill obligations (cooperate) on the worksite Market Efficiency (total surplus net of NEP program costs, unemployment/vacancy rates,...) Market Power (distribution of total net surplus) 22 Experimental Design Treatment Factor: Non-Employment Payment (NEP) Three Tested Treatment Levels: NEP=0, NEP=15, NEP=30 Runs per Treatment: 20 (1 Run = 1000 Generations; 1 Gen.=150 Work Periods) Data Collected Per Run: Network patterns, behaviors, and market performance (reported in detail for generations 12, 50, 1000) 23 Three NEP Treatments in Relation to PD Payoffs NEP=0 < L=10 L=10 < NEP=15 < D=20 D=20 < NEP=30 < C=40 NOTE: Work-site PD payoffs given by: L (Sucker)=10 < D (Mutual-D)=20 < C (Mutual-C)=40 < H (Temptation)=60 24 Market Efficiency Findings As NEP level increases from 0 to 30 higher average unemployment and vacancy rates are observed; KNOWN EFFECT more work-site cooperation observed on average among workers & employers who match. NEW EX POST EFFECT Note: These outcomes have potentially offsetting effects on market efficiency. 25 Efficiency Findings... Market Efficiency (Utility less NEP Program Costs) Averaged Across Generations 12, 50, and 1000 for three different NEP treatments Market Efficiency 90 88 60 NEP 0 15 30 26 Efficiency Findings... NEP=15 yields highest efficiency NEP=0 yields lower efficiency (too much shirking) NEP=30 yields lowest efficiency (UB program costs too high) 27 Multiple Attractors Two distinct attractors observed for each NEP treatment... NEP=0 and NEP=15: First Attractor = Latched network supporting mutual cooperation; Second Attractor = Latched network supporting intermittent defection NEP=30: First Attractor = Latched network supporting mutual cooperation Second Attractor = Completely disconnected network (total coordination failure) 28 The Following Diagrams Report... 1 Two-sided (W-E) network distributions 0=Stochastic fully connected network; 12=Latched in pairs W E W E ... 24=Completely disconnected 2 Worksite behaviors supported by these network outcomes 29 Network Distribution for NEP=0 Sampled at End of Generation 12 Network Distribution for ZeroT:12 20 18 16 Number of Runs 14 12 10 8 6 4 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Network Distance Intermittent Defection Mutual Cooperation 30 Network Distribution for NEP=0 Sampled at End of Generation 50 Network Distribution for ZeroT:50 20 18 16 14 12 10 8 6 4 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Number of Runs Network Distance Intermittent Defection Mutual Cooperation 31 Network Distribution for NEP=0 Sampled at End of Generation 1000 Network Distribution for ZeroT:1000 20 18 16 14 12 10 8 6 4 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Number of Runs Network Distance Intermittant Defection Mutual Cooperation 32 Network Distribution for NEP=15 Sampled at End of Generation 12 Network Distribution for LowT:12 20 18 16 14 12 10 8 6 4 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Number of Runs Network Distance Intermittent Defection Mutual Cooperation 33 Network Distribution for NEP=15 Sampled at End of Generation 50 Network Distribution for LowT:50 20 18 16 14 12 10 8 6 4 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Number of Runs Network Distance Intermittent Defection Mutual Cooperation 34 Network Distribution for NEP=15 Sampled at End of Generation 1000 Network Distribution for LowT:1000 20 18 16 14 12 10 8 6 4 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Number of Runs Network Distance Intermittent Defection Mutual Cooperation 35 Network Distribution for NEP=30 Sampled at End of Generation 12 Network Distribution for HighT:12 20 18 16 14 12 10 8 6 4 2 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Number of Runs Network Distance Intermittent Defection Mutual Cooperation Coordination Failure 36 Network Distribution for NEP=30 Sampled at End of Generation 50 Network Distribution for HighT:50 20 18 16 14 12 10 8 6 4 2 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Number of Runs Network Distance Mutual Cooperation Coordination Failure 37 Network Distribution for NEP=30 Sampled at End of Generation 1000 Network Distribution for HighT:1000 20 18 16 14 12 10 8 6 4 2 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Number of Runs Network Distance Mutual Cooperation Coordination Failure 38 Summary of Findings Changes in NEP systematically affect unemployment, vacancy, worksite behaviors, and welfare outcomes Worker-employer networks tend to be either fully latched in pairs or completely disconnected But even fully latched networks support multiple peaked behavioral distributions (potential pooling problems) 39

Find millions of documents here - Study Guides, Homework Solutions, Papers, Exam Answer Keys and more. Course Hero has millions of course related materials that will enable you to learn better, faster and get an A in all your courses.
Below is a small sample set of documents: