下载文档原格式
家人不同意孩子做家务的英语作文Child Labor in the Home: Balancing Responsibilities and Exploitation.Childhood, a time of innocence and boundless energy, should be cherished for its freedom to play, learn, and explore. However, for some children, the carefree days of youth are marred by the burden of household chores. The debate over whether children should be required to do household chores has been raging for decades, with proponents citing the benefits of fostering responsibility and life skills, while opponents condemn the exploitation of young minds and bodies.Arguments in Favor of Child Labor.Proponents of child labor in the home often argue that it teaches children the importance of responsibility and accountability. By assigning chores to children, parents can instill a sense of duty and pride in their young ones.Children learn the value of hard work and contribution to the family unit. Additionally, practical skills such as cleaning, cooking, and laundry can empower children and prepare them for adulthood.Furthermore, some proponents believe that chores can teach children time management and organization. By having children assist with household tasks, parents can help them develop routines and prioritize their responsibilities. This can lead to improved study habits, increased productivity, and a greater sense of accomplishment.Arguments against Child Labor.Opponents of child labor in the home vehemently condemn the exploitation of young children. They argue that chores can be a burden on children, depriving them of their free time, creativity, and opportunities for relaxation. Children should not be treated as unpaid servants, they contend, but rather as individuals who deserve to enjoy their childhoods.Research has also shown that excessive chores can lead to physical and emotional problems in children. Prolonged exposure to household chemicals, for example, can cause respiratory issues and skin irritation. Additionally, the stress of having to meet parental expectations regarding chores can contribute to anxiety and depression.Balancing Responsibilities and Exploitation.The key to resolving the debate over child labor lies in finding a balance between fostering responsibility and preventing exploitation. Parents should consider the age, maturity, and individual needs of their children when assigning chores. Young children may be able to help with simple tasks such as tidying up their toys or setting the table, while older children can take on more complex responsibilities like vacuuming or mowing the lawn.It is crucial for parents to avoid overburdening children with chores. Children should have ample time for schoolwork, play, and socializing. Chores should be age-appropriate and not interfere with their academic orextracurricular activities.Furthermore, parents should ensure that chores are not used as a form of punishment or control. Children should be rewarded for completing their tasks, and they should not be threatened with consequences if they fail to do so. Open communication and positive reinforcement are essential for fostering a healthy relationship between parents and children regarding household responsibilities.Conclusion.The question of whether children should be required to do household chores is a complex one with no easy answer. While chores can teach children valuable life skills and foster responsibility, it is essential to prevent exploitation and ensure that children's well-being is not compromised. By finding a balance between these two objectives, parents can empower their children with household responsibilities without depriving them of their childhoods.。
Christopher Columbus,an Italian explorer,is widely known for his voyages across the Atlantic Ocean that led to the European discovery of the Americas.His expeditions, which started in the late15th century,marked a significant turning point in world history.1.Early Life and Background:Columbus was born in Genoa,Italy,in1451.His father was a middleclass wool merchant,and from a young age,Columbus showed an interest in the sea and maritime trade.2.Maritime Experience:He went to sea at a young age and gained experience as a sailor. His travels took him to various parts of the Mediterranean and eventually to the service of the Portuguese monarchy,where he further developed his skills in navigation.3.The Plan for a New Route:Columbus believed that by sailing west,he could find a shorter route to Asias riches,bypassing the traditional eastward route around Africa.He spent years trying to convince the monarchs of Europe to support his expedition.4.First Voyage1492:With the support of Queen Isabella and King Ferdinand of Spain, Columbus set sail with three ships:the Santa Maria,the Pinta,and the Niña.On October 12,1492,he landed on an island in the presentday Bahamas,which he named San Salvador.5.Subsequent Voyages:Columbus made a total of four voyages to the Americas.He explored parts of the Caribbean,Central America,and South America,believing he had reached the outskirts of Asia,which is why he called the native inhabitants Indians.6.Impact and Legacy:Columbuss voyages initiated the widespread exchange of plants, animals,people,and diseases between the Americas and the Old World.This period is often referred to as the Columbian Exchange.However,his voyages also led to the colonization and exploitation of the Americas,with devastating consequences for the indigenous populations.7.Controversies:Columbuss legacy is a subject of debate.While he is celebrated for his daring exploration and opening up new worlds,he is also criticized for the brutal treatment of the indigenous people and the introduction of slavery and diseases that decimated native populations.8.Cultural Significance:Columbus Day,celebrated on the second Monday of October in the United States,commemorates his arrival in the Americas.However,the holiday has become controversial,and some regions have replaced it with Indigenous Peoples Day to honor the native cultures that were impacted by European colonization.In conclusion,Christopher Columbuss voyages were a pivotal moment in history, shaping the course of global exploration and cultural exchange.His story serves as a reminder of the complex and often tragic consequences of human ambition and the importance of understanding and respecting diverse cultures.。
蒙特卡洛树法搜索的主要流程The main process of the Monte Carlo Tree Search (MCTS) algorithm involves four main steps. First, the algorithm begins with the selection phase, where it traverses the tree from the root node to a leaf node. Then, it moves on to the expansion phase, where it adds one or more child nodes to the selected node. After that, it goes into the simulation phase, where it carries out a simulated playout from the newly added node. Finally, it enters the backpropagation phase, where it updates the statistics of the nodes from the newly added node to the root.蒙特卡洛树搜索（MCTS）算法的主要流程包括四个主要步骤。
首先，算法从选择阶段开始，从根节点遍历树到叶节点。
然后，它进入扩展阶段，在选定的节点中添加一个或多个子节点。
之后，它进入模拟阶段，在新添加的节点中进行模拟游戏。
最后，它进入反向传播阶段，在新添加的节点到根节点更新节点的统计信息。
During the selection phase, the algorithm uses a selection policy to choose which node to explore next. The selection policy is usually based on a trade-off between exploration and exploitation. It aims tobalance the exploration of different moves or paths with the exploitation of the best known moves or paths. By doing so, the algorithm can efficiently explore the search space and focus on promising areas of the game tree. This helps to improve the overall performance of the algorithm and increase the chances of finding a good solution.在选择阶段，算法使用选择策略来选择下一个要探索的节点。
组织二元性的新机制--跨领域二元性吴晓波;雷李楠;陈颖【摘要】组织二元性中的焦点是企业需要兼顾探索和利用活动，而探索和利用的内涵却在现有的研究中有着不同的理解和定义。
本文在梳理组织二元性研究文献的基础上，从组织学习、战略联盟、技术创新三个领域来分析组织二元性的内涵，并提出“跨领域二元性”这一概念，深化组织二元性领域的理论内涵。
%The key point of organizational ambidexterity is enterprise needs to take both exploration and exploitation into account. However, the connotations of exploration and exploitation hold different understanding and definitions in the existing research. After making the literature review of organizational ambidexterity, this article analyzes organizational ambidexterity from such three areas as organizational learning, strategic alliance and technological innovation. By proposing the cross-domain ambidexterity, this article provides a new way of balancing exploration and exploitation, which could enrich the meaning of ambidexterity and bring guidance to management practices.【期刊名称】《西安电子科技大学学报（社会科学版）》【年(卷),期】2015(000)003【总页数】9页(P1-9)【关键词】组织二元性;跨领域二元性;组织学习【作者】吴晓波;雷李楠;陈颖【作者单位】浙江大学，浙江杭州 310058;浙江大学，浙江杭州 310058;浙江大学，浙江杭州 310058【正文语种】中文【中图分类】F270随着知识经济的到来和信息技术的迅猛发展，越来越多的企业发现自身不仅面临着行业内竞争对手传统的竞争手段，还会遭致未知的新竞争对手以新的方式开展竞争。
科学与自然英语作文Title: The Harmony of Science and Nature。
In the realm of human existence, two forces coexist in perpetual interplay: science and nature. While science represents our quest for understanding through systematic study and experimentation, nature embodies the very essence of our physical world, governed by its own laws and rhythms. Despite their seemingly disparate natures, the relationship between science and nature is one of profound interconnectedness and mutual influence.First and foremost, science serves as a lens through which we perceive and comprehend the intricate workings of the natural world. From the microscopic realm of atoms tothe cosmic expanses of the universe, scientific inquiry unveils the mysteries of nature, unraveling itscomplexities and revealing its underlying order. Through disciplines such as physics, chemistry, biology, and ecology, we gain insights into the fundamental principlesgoverning the universe and the myriad forms of life that inhabit it.Moreover, science empowers us to harness the forces of nature for the betterment of humanity. Through technological innovation and engineering marvels, we leverage our understanding of natural phenomena to enhance our quality of life, advance medical treatment, and mitigate environmental challenges. From the development of vaccines to the harnessing of renewable energy sources, scientific progress has enabled us to adapt, thrive, and even transcend the limitations imposed by our natural environment.However, the relationship between science and nature extends beyond mere exploration and exploitation. At its core, science is deeply rooted in the observation and emulation of natural processes. The principles of physics are derived from the behavior of matter and energy in the natural world, while biological research often draws inspiration from the diversity and adaptability of living organisms. In this sense, science is not merely a humanconstruct imposed upon nature but rather a reflection ofits inherent order and complexity.Conversely, nature serves as both the subject and the object of scientific inquiry, offering an inexhaustible wellspring of inspiration and discovery. The intricate patterns of a snowflake, the rhythmic oscillations of a pendulum, and the elegant symmetry of a flower—all testify to the beauty and intricacy of the natural world, inspiring scientists and artists alike. Moreover, nature serves as a living laboratory, offering countless opportunities for observation, experimentation, and innovation. Whether studying the behavior of animals in their natural habitats or analyzing geological formations millions of years in the making, scientists draw upon the rich tapestry of nature to expand the boundaries of human knowledge.Yet, for all its resilience and adaptability, nature is also fragile and vulnerable in the face of human activity. The indiscriminate exploitation of natural resources, the degradation of ecosystems, and the looming specter of climate change underscore the urgent need for a harmoniousrelationship between science and nature. As stewards of the planet, it is incumbent upon us to embrace a holistic approach that respects the intrinsic value of nature while harnessing the power of science for sustainable development and environmental conservation.In conclusion, the relationship between science and nature is one of profound symbiosis and reciprocity. As we navigate the complexities of the modern world, we must strive to cultivate a deeper understanding of the intricate web of life that sustains us and harness the transformative potential of science to safeguard the integrity and resilience of our natural environment. Only through a harmonious integration of science and nature can we hope to forge a sustainable future for generations to come.。
粒子群算法流程英文Title: The Process of Multi-objective Particle Swarm Optimization AlgorithmThe Multi-objective Particle Swarm Optimization (MOPSO) algorithm is a powerful tool in solving complex optimization problems with multiple objectives. It extends the traditional Particle Swarm Optimization (PSO) by incorporating concepts from multi-objective optimization, allowing for the simultaneous pursuit of multiple conflicting goals. This article outlines the step-by-step process of the MOPSO algorithm.At the outset, the algorithm initializes a population of particles, each representing a potential solution to the problem. The position and velocity of each particle are randomly assigned within the search space. Additionally, an archive is created to store non-dominated solutions discovered during the optimization process.Each particle's fitness is evaluated based on the objective functions of the problem. In a multi-objective context, this involves calculating the values of all objectives for each particle.The particles are then sorted into different ranks based on their non-domination levels. A particle is considerednon-dominated if there is no other particle that outperforms it on all objectives.The non-dominated particles are added to the archive. If the archive exceeds its maximum size, a subset of the most diverse solutions is retained to maintain a balance between convergence and diversity.For each particle, a new position is calculated based on its own best historical position, the global best position from the archive, and the current velocity. The velocity update considers inertia, cognitive, and social components, reflecting both individual experience and social learning.After updating the position, it's essential to ensure that the particles remain within the defined search boundaries. Any particle that moves outside these limits is adjusted accordingly.The algorithm checks whether the termination criteria have been met. These could include a maximum number of iterations, a satisfactory level of convergence, or a predetermined computational budget.Steps 2 through 7 are repeated for a specified number of iterations or until the termination criterion is satisfied. With each iteration, the particles explore the search space, aiming to find better trade-offs among the objectives.The MOPSO algorithm provides a systematic approach to tackling multi-objective optimization problems. By iteratively refining the positions of particles through a combination of personal exploration and social interaction, it converges towards a set of Pareto-optimal solutions. The effectiveness of MOPSO lies in its ability to balance the exploration and exploitation of the search space while managing the complexity of multiple objectives.。
华南农业大学学报 Journal of South China Agricultural University 2024, 45(3): 408-415DOI: 10.7671/j.issn.1001-411X.202305030张亚莉, 莫振杰, 田昊鑫, 等. 基于改进APF-FMT*的农业机器人路径规划算法[J]. 华南农业大学学报, 2024, 45(3): 408-415.ZHANG Yali, MO Zhenjie, TIAN Haoxin, et al. Path planning algorithm of agricultural robot based on improved APF-FMT*[J]. Journal of South China Agricultural University, 2024, 45(3): 408-415.基于改进APF-FMT*的农业机器人路径规划算法张亚莉1,2，莫振杰1，田昊鑫1，兰玉彬2，王林琳3(1 华南农业大学 工程学院, 广东 广州 510642； 2 国家精准农业航空施药技术国际联合研究中心,广东 广州 510642； 3 深圳职业技术大学 人工智能学院, 广东 深圳 518055)摘要: 【目的】解决农业机器人在复杂农业环境下全局路径规划耗时过长、路径最优解求解困难的问题。
【方法】提出一种基于改进人工势场法的快速行进树算法(APF-FMT*)。
首先，在引力势场中引入相对距离，根据与目标点的距离改变引力大小，克服了人工势场法距离目标点过远时引力过大的问题；然后，将FMT*算法与改进人工势场法相结合，采用三阶B样条曲线对路径进行平滑处理；最后，建立3个农业工作地图进行仿真试验。
【结果】仿真结果表明，与FMT*、RRT*和Informed-RRT* 3种算法对比，在地图Map1和Map2中，APF-FMT*都能快速找到良好的解，且随样本数量增加获得的路径解得到改善，搜索时间比其他3种算法减少45%以上；在有狭小通道的Map3中，APF-FMT*、FMT*搜索时间比RRT*和Informed-RRT*减少75%以上，并且获得更好的解。
kl散度中温度参数t的选择Selecting the temperature parameter (t) in the Kullback-Leibler (KL) divergence can greatly impact the performance and effectiveness of various machine learning algorithms. The KL divergence is a measure of how one probability distribution diverges from a second, expected probability distribution. When choosing the value of t for the KL divergence, it is important to consider the specific characteristics of the data and the goals of the analysis.在选择KL散度中的温度参数t时，可以极大地影响各种机器学习算法的性能和有效性。
KL散度是衡量一个概率分布与第二个期望概率分布之间的偏离程度的指标。
在选择KL散度的t值时，重要的是考虑数据的具体特征和分析的目标。
One factor to consider when selecting the temperature parameter for the KL divergence is the complexity of the data distribution. If the data distribution is highly complex and multidimensional, a higher temperature parameter may be more appropriate as it allows for a more flexible and adaptable model. On the other hand, if the datadistribution is simple and one-dimensional, a lower temperature parameter may suffice to capture the nuances of the distribution.选择KL散度的温度参数时要考虑的一个因素是数据分布的复杂性。
contextual bandits详解Contextual Bandits: An In-Depth ExplanationContextual bandits, a type of machine learning algorithm, have gained significant attention in recent years due to their ability to make intelligent decisions in real-time scenarios. In this article, we will delve into the concept of contextual bandits, how they work, and their applications in various domains.At its core, a contextual bandit is a framework wherein an agent aims to learn and optimize its decision-making process based on contextual information. The term "bandit" arises from the classic dilemma faced in gambling, where a player, the agent in this case, must decide which arm of a slot machine, known as the bandit, to pull in order to maximize their cumulative rewards.In the contextual bandit framework, multiple arms represent different actions or strategies the agent can take, and each arm provides a certain reward based on the context or environment it is pulled in. The agent receives a contextual observation or information before each decision and aims to select the best action based on this information to maximize its rewards over time.The challenge lies in not knowing the rewards associated with each action in advance. Therefore, the agent must explore different actions to gather information and exploit actions that have shown to be rewarding in the past. This dilemma between exploration and exploitation is a fundamental trade-off in contextual bandits.The contextual information can be diverse, ranging from user demographics, historical behavior, time of day, or any other relevant contextual factors specific to the problem domain. This additional contextual information allows the agent to learn a personalized policy that adapts to the specific user or environment.Contextual bandits have found applications in various domains such as online advertising, recommendation systems, healthcare, and personalized marketing. In onlineadvertising, for instance, contextual bandits can be utilized to determine which ad to display to a user based on their browsing history, location, and other contextual factors.With advancements in machine learning techniques and algorithms, contextual bandits have become more robust and efficient. Approaches like Thompson Sampling, Upper Confidence Bounds, and Gradient-based methods have proven effective in balancing exploration and exploitation in contextual bandit settings.In conclusion, contextual bandits provide a powerful framework for personalized decision-making in real-time scenarios. By incorporating contextual information, these algorithms enable agents to learn and adapt their actions to maximize rewards over time. With their wide range of applications, contextual bandits continue to play a significant role in improving decision-making processes in various domains.。
话题讨论类作文模板英文回答：Topic Discussion: Exploration vs. Exploitation。
Exploration and exploitation are two distinct concepts that have been the subject of debate for centuries. While exploration involves the process of discovering and understanding new places, cultures, and ideas, exploitation refers to the utilization of resources or people for personal gain.Exploration has played a significant role in human history, driving progress and innovation. It has led to the discovery of new continents, the expansion of knowledge, and the creation of cultural exchange. From the voyages of Christopher Columbus to the space missions of the 21st century, exploration has expanded our understanding of the world and its possibilities.Exploitation, on the other hand, has a negative connotation. It involves the unfair and unsustainable use of resources, often at the expense of others. Examples of exploitation include colonialism, slavery, and the extraction of natural resources without regard for environmental or social consequences.The relationship between exploration and exploitation is complex. While exploration can sometimes lead to exploitation, it is also possible for exploration to be conducted responsibly, with respect for local populations and the environment. Balancing the need for exploration with the potential risks of exploitation is a critical challenge for our time.中文回答：话题讨论，探索与掠夺。
fundamental of reinforcement learning -回复Fundamentals of Reinforcement Learning: An Introduction to the BasicsIntroduction:Reinforcement Learning (RL) is an area of machine learning that focuses on teaching algorithms to make decisions based on trial and error. It is widely used in various fields such as artificial intelligence, robotics, and game theory. RL algorithms learn to make optimal decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. In this article, we will explore the fundamental concepts of reinforcement learning and understand the step-by-step process involved.1. Understanding RL Agent and Environment:In RL, an agent is the learner or decision-maker, and the environment is the setting in which the agent operates. The environment can be as simple as a game or as complex asself-driving cars. The agent interacts with the environment by performing actions, and the environment responds with rewards orpenalties. The goal of the agent is to learn the best actions to maximize the cumulative reward over time.2. Markov Decision Processes (MDPs):MDPs are mathematical models that formalize decision-making problems in RL. They consist of a set of states, actions, transition probabilities, and rewards. The agent starts in a particular state, performs an action, and transitions to a new state according to the transition probabilities. The agent receives a reward based on the state-action pair. MDPs provide a framework for modeling RL problems and allow for the application of various learning algorithms.3. Policy and Value Functions:A policy in RL determines the behavior of the agent. It is a mapping from states to actions and represents the strategy fordecision-making. The policy can be deterministic, where it directly selects an action for each state, or stochastic, where it selects actions with certain probabilities. Value functions, on the other hand, estimate the expected cumulative reward from a particularstate or state-action pair. The value functions help the agent in evaluating the potential returns from different states and actions and are crucial for making optimal decisions.4. Q-Learning Algorithm:Q-Learning is one of the most popular algorithms in RL. It is a model-free method that does not require prior knowledge of the environment's dynamics. The Q-Learning algorithm estimates the action-value function or Q-values, which represent the expected cumulative reward of taking a particular action in a given state. The algorithm iteratively updates the Q-values based on the rewards received and the new information obtained from the environment. By exploring and exploiting different actions, the agent gradually learns the optimal policy.5. Exploration and Exploitation:In RL, exploration refers to the act of trying out new actions to gather information about the environment. Exploitation, on the other hand, involves using the learned knowledge to maximize the cumulative reward. Balancing exploration and exploitation is afundamental challenge in RL. Too much exploration may result in inefficient learning, while too much exploitation may lead tosub-optimal solutions. Various exploration strategies, such as epsilon-greedy and softmax, are used to strike a balance.6. Temporal Difference Learning:Temporal Difference (TD) learning is another key concept in RL. It combines elements of both Monte Carlo methods (which learn from complete episodes) and dynamic programming (which learns from intermediate states). TD learning allows the agent to update its value function estimates after experiencing only a partial trajectory, making it more computationally efficient. TD algorithms, such as SARSA and Q-Learning, use the temporal difference error to update the value function estimates incrementally.7. Deep Reinforcement Learning:Deep Reinforcement Learning (DRL) combines RL with deep neural networks to handle high-dimensional state spaces. Traditional RL algorithms may struggle with complex problems due to the curse of dimensionality. DRL uses deep neural networks as functionapproximators to learn directly from raw sensory inputs. Techniques like Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) have achieved remarkable results in challenging domains such as video games and robotics.Conclusion:Reinforcement Learning is an exciting field that enables machines to learn optimal decision-making strategies through interaction with the environment. By understanding the fundamental concepts, such as agents, environments, policies, value functions, and learning algorithms like Q-Learning, exploration-exploitation trade-offs, temporal difference learning, and deep reinforcement learning, we can build intelligent systems capable of learning from experiences and adapting to new situations. RL has the potential to revolutionize various industries, making it a domain worth exploring and studying further.。
neural processing letters文章模板-回复"Neuroevolution in Robotics: Utilizing Artificial Neural Networks for Autonomous Agents"Introduction:Artificial neural networks (ANNs) have gained significant attention in the field of robotics due to their ability to mimic the complexities of the human brain. One specific area where ANNs have demonstrated remarkable capabilities is in the development of autonomous agents. This article aims to explore the groundbreaking potential of neuroevolution in robotics, providing an in-depth analysis of the neural processing letters research in this domain.1. Evolutionary Algorithms for ANN Training:Evolutionary algorithms (EAs) have proven to be highly effective in training ANNs for autonomous agents. EAs mimic the process of natural selection, enabling the network to evolve over multiple generations. This process involves encoding ANNs as individuals, evaluating their performance using fitness functions, selecting thefittest individuals, and applying genetic operators (e.g., mutation, crossover) to produce the next generation. Through iterative optimization, EAs achieve ANNs that are capable of fulfilling complex tasks in various robotic applications.2. Neuroevolution of Augmenting Topologies (NEAT):The Neuroevolution of Augmenting Topologies (NEAT) algorithm is a prime example of how neuroevolutionary techniques can enhance the capabilities of robotic agents. NEAT utilizes a unique approach where ANNs start with a minimal structure and are evolved by allowing the addition of new neurons and connections throughout the training process. This methodology avoids the need for manual design of network architectures, enabling the evolution of increasingly complex ANNs that can adapt and respond to environmental changes effectively.3. Reinforcement Learning and Neuroevolution:Reinforcement learning (RL) serves as a complementary methodology to neuroevolution in training robotic agents. By utilizing a reward-based system, RL algorithms guide ANNs to learnoptimal behaviors through trial and error. The integration of RL with neuroevolution allows for an intelligent combination of exploration and exploitation in both the policy and the topology search space. This hybrid approach has shown exceptional results in improving the efficiency and adaptability of autonomous agents in complex tasks.4. Case Study: Autonomous Navigation:To further exemplify the impact of neuroevolution in the field of robotics, we focus on the case of autonomous navigation. This problem requires a robotic agent to navigate through a given environment, avoiding obstacles and reaching a specified destination. Neuroevolution techniques, such as NEAT and RL, have been successfully employed to train ANNs capable of tackling this challenging task. Through the process of evolutionary optimization, these agents learn to perceive the environment, make informed decisions, and adapt to dynamic changes, ultimately achieving efficient and reliable autonomous navigation.Conclusion:The use of neuroevolution in robotics, specifically in the training of ANNs for autonomous agents, provides a remarkable opportunity to advance the capabilities of robotic systems. By leveraging evolutionary algorithms, such as NEAT, and integrating reinforcement learning methodologies, researchers continue to push the boundaries of what is possible in terms of robotics autonomy. The case study of autonomous navigation showcases the practical applications and promising results of neuroevolution in robotics. With further advancements in this field, we can expect to witness more sophisticated and adaptive robotic systems that can operate in complex real-world scenarios effectively.。
11 规则后真题111. The Safety of Life at Sea Convention was developed by the ____A.IMDG conferenceB.American Bureau of ShippingC.International Maritime OrganizationD.American Institute of Maritime Shipping2. a red triangular daymark marks____A. the centerline of a navigable channelB. the starboard side of a channelC. prominent object of navigational interest that has not lateral significanceD. an area of a channel where passing another vessel is permitted3. which weather element cannot be measured accurately while on board a moving vessel?A. visibilityB. temperatureC. wind directionD. atmospheric pressure4. whose duty is it to examine and verify the state and condition of the cargo carried on board?A. tally man’sB. marine surveyor’sC. Cargo su rveyor’sD. boarding inspector’s5. . Malacca and Straits is 805km ____ and ____form 64 km in the S to 257 km in the N.A.Long, widensB.Long, wideC.Length, widensD.Length, wide6. the diurnal pressure variation is most noticeable in the ___A. polar regionsB. horse latitudeC. roaring fortiesD. doldrums7.which would you Not use to report the amount of anchor chain out? Three shots___A. at the water’s edgeC. on the bottomD. in the water关联题Every ship of 400 gross tonnage and above and every ship which is certified to carry 15 persons or more engaged in voyages to ports or offshore terminals under the jurisdiction of other Parties and every fixed and floating platform engaged in exploration and exploitation of the sea-bed shall be provided with a garbage record book.Each discharge operation, or completed incineration, shall be recorded in the garbage record book and signed for on the date of the incineration or discharge by the officer in charge. Each completed page of the garbage recorded book shall be signed by the master of the ship. The entries in the garbage recorded book shall be at least in English, French and Spanish.The entry for each incineration or discharge shall included date and time, position of the ship, description of the garbage and the estimated amount incinerated or discharged.The garbage record book shall be kept on board the ship and in such a place as to be available for inspection in a reasonable time. This document shall be preserved for a period of two years after the last entry is made on the record.In the event of discharge, escape or accidental loss, an entry shall be made in the garbage record book of the circumstances of, and the reasons for, the loss.(1)___shall not be provided with a Garbage Record Book.A. every ship of 400 gross tonnage and aboveB. every ship which is certified to carry 15 persons or moreC. offshore terminals under the jurisdiction of other Parties.D. a fishing ship of 350 gross tonnage(2) the entry for each incineration or discharge shall not included___A. date and timeB. position of the shipC. particulars of the shipD. description of the garbage(3) the garbage record book shall be kept on board the ship for___ after the last entry is made on the record.A. one yearB. two yearsC. three yearsD. five years(4) the entries in the garbage record book shall not be ___according to the passage.B. in ChineseC. in SpanishD. in French8. In which condition should not seafarers be permitted to perform watch keeping duties?A.Seafarers are found to be under the influence of drugs or alcoholB.Seafarers are found in high spiritC.Seafarers are found to need enough restD.Seafarers are found wearing a glasses.9. details of coast radio stations and radar stations can be found in ___A. admiralty chartB. admiralty light chartC. admiralty list of radio signalsD. admiralty notices to mariners10. ___on still water indicates how loading weight distribution affects on longitudinal strength.A. bending momentB. local strengthC. shear forceD. displacement11. when crossing a front, isobars tend to___A. change from smooth curves within the air mass to sharp bends at the front（压差大了）B. change from sharp bends within the air mass to smooth curves at the frontC. pass smoothly across the front with no changeD. become closer together at the front and pass through in straight lines12. What are essential to ensure the effectives implementation of the ship security plan?A.Controlling access to the shipB.Training and drillsC.Controlling the restricted areasD.Monitoring the ship at times13. a lookout can leave his station___A. at the end of the watchC. only when properly relievedD. 15 minutes before the end of the watch14. the velocity of the wind, its steady direction, and the amount of time it has blown determines a wind driven current’s ____A. temperatureB. densityC. deflectionD. speed15. a local wind which occurs during the daytime and is caused by the different rates of warming of land and water is a ___A. foehnB. ChinookC. land breezeD. sea breeze16. after a cold front passes the barometric pressure usually___A. fluctuatesB. remains the sameC. remains the same, with clouds forming rapidlyD. rises, often quite rapidly, with clearing skies.17. Furnishings for recreational facilities should ______include a bookcase and facilities for reading, writing and, where practicable, games.A.as a minimumB.as a maximumC.as a ruleD.as a result18. chart legends printed in capital letters show that the associated landmark is ___A. conspicuous’B. inconspicuousC. a government facility or stationD. a radio transmitter19. admiralty notices to mariners, containing information for the mariners and enabling them to keep their charts and books corrected for the latest information, are issued daily and published___A. weekly editionsB. quarterly editionC. annual edition。
reinforcement learning英文文献Title: Reinforcement Learning: An Overview of English LiteratureIntroduction:Reinforcement Learning (RL) is a subfield of machine learning that focuses on training agents to make sequential decisions by interacting with an environment. In this article, we will provide an overview of the English literature on reinforcement learning, discussing its key concepts, algorithms, applications, challenges, and future directions.1. Key Concepts of Reinforcement Learning:1.1 Definition and Components of RL:- Definition of RL: RL is a learning paradigm where an agent learns to take actions in an environment to maximize cumulative rewards.- Components of RL: RL consists of an agent, an environment, a policy, rewards, and value functions.1.2 Markov Decision Process (MDP):- Explanation of MDP: MDP is a mathematical framework used to model RL problems, where an agent interacts with an environment in discrete time steps.- Components of MDP: States, actions, transition probabilities, rewards, and discount factor.1.3 Exploration and Exploitation:- Exploration: The agent explores the environment to discover new strategies and actions that may lead to higher rewards.- Exploitation: The agent exploits the learned knowledge to make decisions that maximize the expected rewards.1.4 Reward Functions and Value Functions:- Reward Function: Determines the immediate feedback an agent receives after taking an action.- Value Function: Estimates the expected cumulative future rewards an agent will receive from a particular state or action.2. Reinforcement Learning Algorithms:2.1 Q-Learning:- Explanation of Q-Learning: Q-Learning is a model-free RL algorithm that learns an action-value function to make optimal decisions.- Exploration vs. Exploitation in Q-Learning: Balancing exploration and exploitation using epsilon-greedy or softmax policies.2.2 Policy Gradient Methods:- Explanation of Policy Gradient Methods: These methods directly optimize the policy function to maximize the expected rewards.- REINFORCE Algorithm: Basic policy gradient algorithm using Monte Carlo sampling.2.3 Deep Q-Network (DQN):- Introduction to DQN: Combines Q-Learning with deep neural networks to handle high-dimensional state spaces.- Experience Replay and Target Network: Techniques used to stabilize DQN training.3. Applications of Reinforcement Learning:3.1 Game Playing:- RL in Atari Games: Deep RL algorithms achieving human-level performance in various Atari games.- AlphaGo: Reinforcement learning-based algorithm that defeated world champion Go players.3.2 Robotics:- RL for Robot Control: Training robots to perform complex tasks such as grasping objects or walking.- Sim-to-Real Transfer: Transferring learned policies from simulations to real-world robotic systems.3.3 Autonomous Driving:- RL for Autonomous Vehicles: Teaching self-driving cars to navigate traffic and make safe decisions.- Safe Exploration: Ensuring RL agents learn policies that do not compromise safety.4. Challenges in Reinforcement Learning:4.1 Exploration vs. Exploitation Trade-off:- The challenge of balancing exploration to discover optimal policies while exploiting known strategies.4.2 Sample Efficiency:- RL algorithms often require a large number of interactions with the environment to learn effective policies.4.3 Generalization:- Extending learned policies to new, unseen environments or tasks.5. Future Directions in Reinforcement Learning:5.1 Multi-Agent RL:- Studying RL in settings where multiple agents interact and learn simultaneously.5.2 Hierarchical RL:- Developing RL algorithms that can learn and exploit hierarchies of actions and goals.5.3 Meta-Learning:- Training RL agents to quickly adapt to new tasks or environments.Conclusion:Reinforcement Learning is a dynamic field of research that has witnessed significant advancements in recent years. This article provided an overview of the key concepts, algorithms, applications, challenges, and future directions in reinforcement learning, showcasing its potential to revolutionize various domains, including gaming, robotics, and autonomous driving. As researchers continue to explore new techniques and algorithms, the future of reinforcement learning holds great promise for solving complex decision-making problems.。
第三章保护和改善环境CHAPTER III PROTECTION AND IMPROVEMENT OF THE ENVIRONMENT第十六条地方各级人民政府，应当对本辖区的环境质量负责，采取措施改善环境质量。
Article 16. The local people's governments at various levels shall be responsible for the environment quality of areas under their jurisdiction1 and take measures to improve the environment quality.第十七条各级人民政府对具有代表性的各种类型的自然生态系统区域，珍稀、濒危的野生动植物自然分布区域，重要的水源涵养区域，具有重大科学文化价值的地质构造、著名溶洞和化石分布区、冰川、火山、温泉等自然遗迹，以及人文遗迹、古树名木，应当采取措施加以保护，严禁破坏。
Article 17. The people's governments at various levels shall take measures to protect regions representing various types of natural ecological2 systems, regions with a natural distribution of rare and endangered wild animals and plants, regions where major sources of water are conserved3, geological structures of major scientific and cultural value, famous regions where karst caves and fossil deposits are distributed, traces of glaciers4, volcanoes and hot springs, traces of human history, and ancient and precious trees. Damage to the above shall be strictly5 forbidden.第十八条在国务院、国务院有关主管部门和省、自治区、直辖市人民政府划定的风景名胜区、自然保护区和其他需要特别保护的区域内，不得建设污染环境的工业生产设施；建设其他设施，其污染物排放不得超过规定的排放标准。
BALANCING EXPLORATION AND EXPLOITATION INALLIANCE FORMATIONDOVEV LAVIEUniversity of Texas at Austin LORI ROSENKOPF University of PennsylvaniaDo firms balance exploration and exploitation in their alliance formation decisions and,if so,why and how?We argue that absorptive capacity and organizational inertia impose conflicting pressures for exploration and exploitation with respect to the value chain function of alliances,the attributes of partners,and partners’network positions.Although path dependencies reinforce either exploration or exploitation within each of these domains,we find that firms balance their tendencies to explore and exploit over time and across domains.Scholars studying exploration and exploitation in organizational learning have assumed a strategic posture by recognizing the essential trade-offs firms make in undertaking these activities,yet little is known about the organizational mechanisms that drive firms’tendencies to engage in either activity or about whether and how firms balance the two activities.The fundamental conceptualizations highlighting the merits of balancing conflicting needs for exploration and exploitation (Levinthal &March,1993;March,1991)have been incorporated only in simulation studies (e.g.,Levinthal,1997;Rivkin &Siggelkow,2003)or elucidated by anec-dotal evidence (e.g.,Tushman &O’Reilly,1997).Studies such as those cited have noted that alter-native organizational forms,such as decentralized versus centralized structures and organic versus mechanistic ones,are better suited for engaging in either exploration or exploitation within firms’or-ganizational boundaries (Brown &Eisenhardt,1997;Nickerson &Zenger,2002;Siggelkow &Levinthal,2003).However,they do not address the question of balance in interfirm relationships.Sim-ilarly,a host of empirical studies in the alliance literature have struggled to identify industry con-ditions or clusters of firms that demonstrate ten-dencies to either explore or exploit,paying less regard to whether balance can be achieved.We attempt to fill this gap in organizational learning research by offering theory and evidence that dem-onstrate why and how firms balance these tenden-cies over time and across domains.We focus on the challenges of balancing explora-tion and exploitation in alliance formation deci-sions (Koza &Lewin,1998)following the growing interest in interorganizational learning.In this con-text,most studies have focused on external indus-try forces,suggesting that turbulence and market uncertainty may generate either exploitation (Beck-man,Haunschild,&Phillips,2004;Rothaermel,2001b),exploration (Rowley,Behrens,&Krack-hardt,2000),or both (Koza &Lewin,1998).Park,Chen,and Gallagher (2002)noted that only re-source-poor firms form exploitation alliances in turbulent industries.Yet even studies that examine firm characteristics have produced mixed evidence on the antecedents of exploration and exploitation.For instance,Rothaermel and Deeds (2004)noted that exploitation increases with firm size,whereas Beckman and her coauthors (2004)showed that firm size also contributes to exploration.TheseWe thank special issue coeditor Ken Smith and three anonymous AMJ reviewers for their helpful comments.We benefited from the feedback received from Ranjay Gulati,Pam Haunschild,George Huber,Dan Levinthal,Toby Stuart,and Jim Westphal.Additional feedback was received from participants in the 2005Harvard Business School Corporate Entrepreneurship Conference and par-ticipants of management colloquia held at the Wharton School,University of Pennsylvania,and the McCombs School of Business,University of Texas at Austin.We also thank Christopher Ray and Mike Hendron for their research assistance.The data used in this study were derived primarily from Dovev Lavie’s doctoral disserta-tion,“The Interconnected Firm:Evolution,Strategy,and Performance”(University of Pennsylvania,2004).We are grateful for the financial support received from the Mack Center for Technological Innovation at the Wharton School.An abbreviated prior version of this paper was published in the Best Paper Proceedings of the 2005annual meeting of the Academy of Management,in Honolulu.Academy of Management Journal 2006,Vol.49,No.4,797–818.797studies offer interesting insights but focus on dif-ferent conceptualizations of the exploration-exploi-tation construct.We posit that prior research on the antecedents of exploration and exploitation has produced incon-sistent evidence because each study examined ex-ploration and exploitation within a single domain, disregarding the conflicting organizational pres-sures that influence learning in various domains.In this study,we explicitly distinguish three domains in which exploration and exploitation can be pur-sued and balanced.The alliance literature has tra-ditionally associated exploration and exploitation with the value-adding activities of alliances—that is,the value chain function that they serve—thus conceptualizing a“function domain.”Taking this course,researchers have identified knowledge-gen-erating R&D alliances as exploration alliances and knowledge-leveraging marketing alliances as ex-ploitation alliances(Koza&Lewin,1998;Rothaer-mel,2001b).Setting a somewhat different course, we also encompass the network positions of part-ners(conceptualizing a structure domain)and their profiles(conceptualizing an attribute domain). Structure exploration refers to a firm’s decision to form alliances with partners with whom it has no prior ties(Beckman et al.,2004),whereas attribute exploration refers to a firm’s forming alliances with partners whose organizational attributes consider-ably differ from those of its prior partners.In our framework,we further acknowledge the interde-pendence between exploration and exploitation (Levinthal&March,1993)by conceptualizing these activities as resting on a single continuum rather than prevailing as two independent organizational choices.Our main contribution to theory involves delin-eating distinct domains of exploration-exploitation and advancing the notion that firms balance explo-ration and exploitation over time within domains as well as across these domains.For example,a firm may engage in an R&D alliance(function ex-ploration)with a prior partner(structure exploita-tion)who differs in size and industry focus from the firm’s other partners(attribute exploration). Moreover,a firm may shift from exploitation to exploration or vice versa within domains over time (e.g.,transitioning from prior partners to new part-ners).To further advance this theory,we supple-ment the traditional focus on external industry forces by arguing that firms face internal organiza-tional pressures for exploration-exploitation. Whereas organizational inertia(Hannan&Free-man,1984)fosters exploitation,absorptive capacity (Cohen&Levinthal,1990)encourages exploration. We posit that although firms encounter challenges in balancing these conflicting pressures within do-mains,they can reconcile these pressures by dy-namically balancing exploration and exploitation across domains.Our analysis of a comprehensive sample of alliances formed by United States–based software firms between1990and2001provides empirical support for these ideas.THEORY AND HYPOTHESESThe exploration-exploitation framework distin-guishes two broad patterns of learning behaviors. March defined them as follows:“Exploration in-cludes things captured by terms such as search, variation,risk taking,experimentation,play,flexi-bility,discovery,innovation.Exploitation includes such things as refinement,choice,production,effi-ciency,selection,implementation,execution”(1991:71).Levinthal and March added that explo-ration involves“a pursuit of new knowledge,”whereas exploitation involves“the use and devel-opment of things already known”(1993:105).More recently researchers have elaborated these ideas by considering their implications not only for intra-but also for interorganizational learning(e.g.,Child, 2001;Grant&Baden-Fuller,2004;Holmqvist,2003; Ingram,2002;Lane&Lubatkin,1998;Larsson, Bengtsson,Henriksson,&Sparks,1998).They have recognized that collaboration with partners facili-tates learning by accessing new knowledge residing outside a firm’s boundaries and by collaboratively leveraging existing knowledge with partners.Thus, alliances,which are voluntary arrangements among independent firms involving exchange,sharing,or joint development or provision of technologies, products,or services(Gulati,1998),have become a noteworthy vehicle for exploration and exploitation. The Three Domains of Exploration-Exploitation in Alliance FormationWe identify three separate domains of explora-tion and exploitation that together describe an alli-ance.We begin by asking three questions:What is the value chain function of the alliance(function)? Whom does the focal firm partner with(structure)? To what extent does the firm’s partner differ from prior partners(attribute)?Table1summarizes these distinctions.Function exploration-exploitation.The explora-tion-exploitation framework was introduced in the interorganizational context by Koza and Lewin (1998),who noted that firms may form alliances to exploit existing knowledge or to explore new op-portunities.Subsequent research has thus focused on the value chain function that alliances serve798AugustAcademy of Management Journal(Koza&Lewin,2000;Park et al.,2002;Rothaermel, 2001b;Rothaermel&Deeds,2004).Firms that en-gage partners in R&D that may lead to innovative technologies and applications can be said to partic-ipate in exploration,whereas firms that rely on alliances for commercializing and using existing technologies or employing complementary partner capabilities undertake exploitation.In this sense, exploration alliances engage in upstream activities of the value chain,enabling partners to share tacit knowledge and develop new knowledge.In con-trast,exploitation alliances engage in downstream activities such as commercialization and marketing that leverage and combine partners’existing capa-bilities through exchanges of explicit knowledge (Rothaermel,2001b).The distinction between ac-quiring and generating new knowledge through ex-ploration and accessing,integrating,and imple-menting existing knowledge through exploitation (Grant&Baden-Fuller,2004)has been thus linked to firms’polar tendencies to engage in R&D alli-ances versus marketing alliances(Park et al.,2002; Rothaermel,2001b;Rothaermel&Deeds,2004). Structure exploration-exploitation.The struc-ture domain of exploration-exploitation takes into account the network positions of a firm’s partners. Recurrent alliances between firms are considered a form of exploitation,and alliances formed with new partners are considered exploration.When a firm forms recurrent alliances with a select group of partners,it can rely on existing arrangements and channels to facilitate access and transfer of knowl-edge already prevailing in its immediate alliance network.In this regard,Beckman and colleagues (2004)argued that forming additional alliances with existing partners is a form of exploitation in which a firm reinforces its existing relationships in order to use its current knowledge base.Hence,the proximate network positions of partners facilitates the flow of knowledge and information and en-hances the efficiency of collaboration(Verspagen& Duysters,2004).By forming alliances with familiar partners,firms can also rely on prior experience and interfirm trust to enhance the predictability and reliability of collaboration(Chung,Singh,& Lee,2000;Gulati,1995a;Gulati&Gargiulo,1999; Li&Rowley,2002);such a pattern of alliance for-mation corresponds to March’s(1991)notion of exploitation.In contrast,when partners have no prior ties to a firm,the firm cannot rely on direct experience with these partners,but it can broaden its reach and seek knowledge that cannot be chan-neled through its immediate network.In keeping with March(1991),because a search for partnersTABLE1Domains of Exploration-ExploitationDomain Function Structure AttributeAnswers the question What value chain function doesthe alliance serve?Whom does the firm partnerwith?To what extent does the partnerdiffer from prior partners?Focus Alliance type Network structure Partner profileExploration(March,1991) (search,variation,risktaking,experimentation,play,flexibility,discovery,innovation)Forming a knowledge-generatingR&D allianceForming an alliance with anew partner that has noprior ties to the firmForming an alliance with apartner whose organizationalattributes differ from those ofprior partnersExploitation(March,1991) (refinement,choice,production,efficiency,selection,implementation,execution)Forming a knowledge-leveragingmarketing/production allianceForming recurrent allianceswith a partner that hasprior ties to the firmForming an alliance with apartner whose organizationalattributes are similar to thoseof prior partnersContent of learned knowledge Value chain knowledge such asnew technologies or marketinformation and expertise inexisting technologies Remote knowledge andinformation on partners’identities andaccessibility or immediateknowledge and in-depthfamiliarity with specificpartnersExposure to organizationaldiversity or specialization ina specific set of partnerattribute configurationsRelevant references Koza&Lewin(1998);Rothaermel(2001);Rothaermel&Deeds(2004)Baum,Rowley,Shipilov,&Chuang(2005);Beckman,Haunschild&Phillips(2004);Verspagen&Duysters(2004)Gulati,Lavie,&Singh(2003);McGrath(2001);Darr&Kurtzberg(2000)2006799Lavie and Rosenkopfbeyond a firm’s local network offers new opportu-nities yet greater uncertainty and risk,we concep-tualized it as a form of exploration.Attribute exploration-exploitation.Unlike the function domain,which defines the nature of alli-ance relationships,and the structure domain, which relates to the prior network positions of part-ners,the attribute domain refers to intertemporal variance in the organizational attributes of a firm’s partners.Following March(1991),we associate exploration with experimentation and variation in routines,processes,technologies,and applications. Exploration enhances adaptation to environmental changes by increasing variance in these organiza-tional attributes(McGrath,2001)and by supporting “long jumps”or reorientations(Levinthal,1997) that enable a firm to adopt new attributes and attain new knowledge outside its domain(Rosenkopf& Nerkar,2001).A deviation from a systematic pat-tern of alliance formation with partners that share certain organizational attributes is thus considered an exploratory behavior.In contrast,when a firm persistently forms new alliances with partners that are similar to its prior partners with respect to attributes such as size and industry focus,it can apply established heuristics and effective gover-nance mechanisms for assimilating external knowl-edge(Darr&Kurtzberg,2000)and can also effi-ciently accumulate and apply its partnering experience in the learning process(Gulati,Lavie,& Singh,2003;Hoang&Rothaermel,2005).Such per-sistence in alliance formation leads to repetition-based improvement,experiential learning,and spe-cialization,which are associated with exploitation (Levinthal&March,1993).Within the attribute do-main,firms’alliance networks range from exhibit-ing consistency in partners’attributes(exploita-tion)to showing frequent deviation from such a pattern(exploration).The three domains are conceptually distinct,in part,because the content of learned knowledge var-ies across domains(see Table1).Nevertheless,they may be empirically related.Normative versus Behavioral Perspectives on Balancing Exploration and ExploitationThe notion of balance refers to equilibrium be-tween conflicting tendencies.Existing research re-veals a striking contrast between normative as-sumptions and behavioral tendencies with respect to the balance between exploration and exploita-tion.Prescriptions about whether firms should strive to manage the trade-off between exploration and exploitation are inconsistent with observations about firms’tendencies to balance these activities in actual practice.On the one hand,researchers have normatively assumed that firms should seek to balance exploration and exploitation because both short-term productivity and long-term innova-tion are essential for organizational success and survival(March,1991:87).They have urged firms to pursue both effectiveness and efficiency and to integrate organizational renewal and control, which can be correspondingly enhanced via explo-ration and exploitation.Rivkin and Siggelkow highlighted“the need for an organization to strike the balance between search and stability”(2000: 308),and Siggelkow and Levinthal explicitly re-ferred to the“premise that adaptive entities are charged to maintain a balance of exploration and exploitation”(2003:651).Similarly,Tushman and O’Reilly argued that“organizations can sustain their competitive advantage by operating in multi-ple modes simultaneously—managing for short-term efficiency by emphasizing stability and con-trol,as well as for long-term innovation by taking risks and learning by doing”(1997:167).On the other hand,despite the undesirable out-comes and self-destructive nature of adaptive pro-cesses(March,1991),failure and success traps may lead to excessive exploration or exploitation,re-sulting in imbalance(Levinthal&March,1993). Thus,in practice,researchers have long recognized the obstacles that firms face when simultaneously pursuing exploration and exploitation,highlight-ing the contradictory natures of activities designed to achieve efficiency and those aimed at flexibility and adaptation(Abernathy,1978).1Firms may seek to overcome these internal organizational trade-offs by engaging in mergers and acquisitions;these ac-tivities yield loosely coupled subunits that are not bound by the same routines and culture as the parent firms and thus maintain buffers between exploratory and exploitative activities.Alliances can also serve as vehicles allowing a firm to explore external opportunities while maintaining an inte-grated internal organization.Nonetheless,empiri-1Organizational research suggests that firms cope with these trade-offs by frequently alternating between incon-sistent organizational designs(Brown&Eisenhardt, 1997),establishing buffers between specialized subunits (Christensen,1998;Levinthal,1997),or maintaining am-bidextrous organizations that integrate these culturally and organizationally differentiated subunits at the corpo-rate level(Benner&Tushman,2003;Tushman& O’Reilly,1997).These ideas are mostly normative be-cause they entail substantial implementation challenges and require empirical validation to determine whether they indeed lead to a balance between exploration and exploitation.800AugustAcademy of Management Journalcal research indicates that exploitation crowds out exploration(Benner&Tushman,2002;Sorenson& Stuart,2001).In particular,alliance research offers evidence on firms’tendencies to focus on one of these types of activity,explaining what may lead to imbalance, rather than to balance,between exploration and exploitation.The bulk of studies have highlighted exogenous industry forces,such as industry turbu-lence and market uncertainty,that exacerbate firms’tendencies to explore or exploit in their alli-ances(Beckman et al.,2004;Rothaermel,2001b), Even those studies that identify firm characteristics that generate idiosyncratic tendencies to explore or exploit under certain industry conditions(Park et al.,2002;Rothaermel,2001b;Rothaermel&Deeds, 2004)shed almost no light on the organizational mechanisms that guide these tendencies,the trade-offs that they entail,and firms’attempts to balance these tendencies in alliance formation decisions. The balancing of exploration and exploitation in particular domains of alliance formation has thus remained a normative assumption rather than a proven behavioral pattern.In this study,we begin to reconcile the discrepancy between this assump-tion and firms’behavioral tendencies. Conflicting Organizational Pressures for Exploration and ExploitationIn studying whether and how firms balance ex-ploration and exploitation in alliance formation de-cisions,one should consider not only external stimuli in the form of industry conditions but also the internal organizational pressures that guide firms’responses to these stimuli.These fundamen-tal pressures can influence firms’tendencies even in stable industry conditions(Nickerson&Zenger, 2002).In the organizational learning perspective (Levitt&March,1988;March,1991),inertia impels firms toward exploitation,whereas search activi-ties,backed by absorptive capacity,drive explora-tion.The balancing of exploration and exploitation in alliances is challenging given the simultaneous co-existence of these conflicting organizational pressures.Pressures for exploitation.Pressures for exploi-tation often derive from organizational inertia, which is evident“when the speed of reorganization is much lower than the rate at which environmen-tal conditions change”(Hannan&Freeman,1984; 151).Inertia results from internal forces,such as irreversible managerial commitments and historic decisions,as well as from external forces,such as institutional legitimation(Hannan&Freeman, 1984).Inertia intensifies as established routines and skills become embedded in decision-making processes and are applied almost automatically in response to external stimuli(Nelson&Winter, 1982).When a new problem arises,the firm with inertia engages in local search for relevant experi-ences(Cyert&March,1963;Gavetti&Levinthal, 2000),which yields consistent responses.Hannan and Freeman(1984)noted that inertia elicits ac-countable,reproducible,and reliable organization-al outcomes and thus reduces uncertainty and vari-ability in accordance with March’s(1991)notion of exploitation.In the context of alliances,inertial pressures encourage firms to rely on organizational routines for selecting partners,establishing alliance governance mechanisms,allocating resources,and coordinating and monitoring alliances(Kale,Dyer, &Singh,2002).Hence,inertia may independently inhibit exploration in one or more domains of alli-ance formation.First,in the function domain,firms that commit to existing technologies(Burgelman,1994;Kelly& Amburgey,1991)are less likely to explore new technologies through their alliances.Accordingly, Rothaermel(2001b)found that incumbents in the pharmaceutical industry benefited by exploiting complementary assets rather than by exploring new technologies with biotechnology partners.Hence, firms may tend to apply their existing knowledge rather than incur the extensive learning costs of knowledge-generating R&D alliances.Inertial pres-sures to reduce technical uncertainty and organiza-tional risk further limit firms’engagements in knowledge-generating alliances because these alli-ances entail substantially more interaction,collab-oration,and exchange of tacit knowledge than do marketing alliances(Rowley et al.,2000).Hence, organizational inertia may facilitate exploitation in that domain.Second,inertia may reduce structure exploration by promoting partner-selection rou-tines that impel firms to enhance the predictability, stability,and reliability of their alliances.Firms can pursue these objectives by forming recurrent alliances with prior partners that are instituted on familiarity,trust,and established collaboration practices(Gulati,1995a).Thus,inertia favors exist-ing partners despite the potential merits of new partners,resulting in structure exploitation.Fi-nally,even when partner selection routines fail to yield relevant partners,firms may still leverage es-tablished routines to identify partners that match a certain profile.In so doing,they specialize and become efficient in managing alliances,thus rein-forcing attribute exploitation.Pressures for exploration.Whereas inertia drives firms’tendencies to exploit,absorptive ca-pacity facilitates counterpressures by furnishing2006801Lavie and Rosenkopfthe mechanism via which firms can identify the need for and direction of exploratory activities.Ex-ploration is guided not only by inventing but also by learning from others(Huber,1991;Levitt& March,1988)and by employing external knowl-edge(March&Simon,1958).Absorptive capacity, defined as the ability to value,assimilate,and ap-ply external knowledge(Cohen&Levinthal,1990), helps firms identify emerging opportunities and evaluate their prospects,thus enhancing explora-tion.It adjusts firms’aspiration levels,so that they become attuned to learning opportunities and more proactive in exploring them.Indeed,prior research has demonstrated how absorptive capacity en-hances organizational responsiveness and directs scientific and entrepreneurial discovery(Deeds, 2001;Rosenkopf&Nerkar,2001).It also increases the likelihood of identifying external opportunities and can therefore lead to exploration in one or more domains of alliance formation.First,absorptive capacity motivates the search for new technologies and the assimilation of exter-nal knowledge,thus facilitating the formation of knowledge-generating R&D alliances.Although ex-ternal knowledge(Huber,1991)can be grafted through corporate acquisitions or employee recruit-ment,R&D alliances offer a cost-efficient and time-sensitive mode of learning(Kumar&Nti,1998). Hence,absorptive capacity leads to function explo-ration.Second,absorptive capacity encourages the pursuit and assimilation of external knowledge and thus motivates firms to identify new partners that can furnish such knowledge.Firms can broaden their knowledge bases by forming alliances with partners with whom they have no prior ties.Fur-ther,absorptive capacity reinforces structure explo-ration since it enhances receptivity to external knowledge and enables firms to apply and internal-ize the knowledge learned from new partners (Mowery,Oxley,&Silverman,1996).For similar reasons,absorptive capacity encourages firms to enrich their knowledge bases by seeking partners that differ from their prior partners with respect to attributes such as size and industry focus under the assumption that these partners offer access to unique knowledge bases and experiences.Thus, absorptive capacity extends the range of partnering opportunities and enables firms to communicate, understand,and collaborate more effectively with a diverse group of partners(Lane,Salk,&Lyles, 2001).Consequently,firms can experiment with new and characteristically different partners.The challenge of balance.The tension between inertia and absorptive capacity sheds light on the inherent trade-offs between exploration and exploi-tation that prevail within specific domains.These trade-offs emerge not only because of the need to allocate resources for refinement versus develop-ment of technologies or because of the viability of immediate short-term returns versus uncertain long-term returns(March,1991).The challenge of balance in the function,structure,and attribute domains also derives from the fundamentally con-flicting domain-specific pressures imposed by in-ertia and absorptive capacity.Inertia reinforces learning from firms’own experience,but absorp-tive capacity enhances receptivity to external knowledge and thus promotes learning from out-siders(Levitt&March,1988).Whereas absorptive capacity leads to variation,inertia leads to empha-sis on the selection and retention stages of the learning cycle(Zollo&Winter,2002).Unlike ab-sorptive capacity,which generates alternatives and may result in long jumps,inertia compels local search and choice(Gavetti&Levinthal,2000).By investing in absorptive capacity firms attenuate in-ertial forces and constrain their ability to refine and enhance the efficiency of existing routines(March, 1991).As firms develop organizational routines and submit to inertial forces,they subdue their absorptive capacity and adaptability to unfolding environmental events(Hannan&Freeman,1984). Contradicting the normative assumption of bal-ance,we thus expect either exploration or exploi-tation to dominate alliance formation decisions at any given time in specific domains.Two questions remain open:(1)Which activity is likely to domi-nate in each domain?and(2)How can firms bal-ance their exploration and exploitation activities when forming alliances?Path Dependencies in Exploration and Exploitation within DomainsThe tendency to underscore either exploration or exploitation within domains can be ascribed to path dependencies,whereby“a firm’s previous in-vestments and its repertoire of routines(its‘his-tory’)constrain its future behavior”(Teece,Rumelt, Dosi,&Winter,1994:17).Path dependence in ex-ploitation emerges because inertia facilitates rou-tine-based experiential learning.Specifically,a firm’s routines represent persistent patterns of be-havior based on past experience(Nelson&Winter, 1982)that are“the outcome of trial and error learn-ing and the selection and retention of prior behav-iors”(Gavetti&Levinthal,2000:113).Routines that become parts of firms’repertoires are likely to be those that have been previously shown to produce favorable outcomes.In turn,these outcomes may lead to path dependence because the frequency of employing a routine increases its efficiency and the802AugustAcademy of Management Journal。
